<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luo, Na</style></author><author><style face="normal" font="default" size="100%">Weng, Wenguo</style></author><author><style face="normal" font="default" size="100%">Xu, Xiaoyu</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Fu, Ming</style></author><author><style face="normal" font="default" size="100%">Sun, Kaiyu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California</style></title><secondary-title><style face="normal" font="default" size="100%">Science of The Total Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Science of The Total Environment</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational fluid dynamics siumlation</style></keyword><keyword><style  face="normal" font="default" size="100%">human exposure risk</style></keyword><keyword><style  face="normal" font="default" size="100%">indoor air quality</style></keyword><keyword><style  face="normal" font="default" size="100%">NAPA wildfire</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">respiratory injury</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-10-2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">686</style></volume><pages><style face="normal" font="default" size="100%">1251 - 1261</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires—by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviours by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, Wei</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xu, Ning</style></author><author><style face="normal" font="default" size="100%">Xu, Xiaodong</style></author><author><style face="normal" font="default" size="100%">Chen, Jiayu</style></author><author><style face="normal" font="default" size="100%">Shan, Xiaofang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Building and Environment</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data fusion</style></keyword><keyword><style  face="normal" font="default" size="100%">Feature selection</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Physics-based model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-09-2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">162</style></volume><pages><style face="normal" font="default" size="100%">106280</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span&gt;Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.&lt;/span&gt;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li, Han</style></author><author><style face="normal" font="default" size="100%">Hong, Tianzhen</style></author><author><style face="normal" font="default" size="100%">Sofos, Marina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title><short-title><style face="normal" font="default" size="100%">Energy and Buildings</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">infiltration</style></keyword><keyword><style  face="normal" font="default" size="100%">Inverse problems</style></keyword><keyword><style  face="normal" font="default" size="100%">people count</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor data</style></keyword><keyword><style  face="normal" font="default" size="100%">zone air parameters</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-09-2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">198</style></volume><pages><style face="normal" font="default" size="100%">228 - 242</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Physics-based simulation of energy use in buildings is widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO2 concentration given other easy-to-measure model parameters are known.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Blum</style></author><author><style face="normal" font="default" size="100%">Filip Jorissen</style></author><author><style face="normal" font="default" size="100%">Sen Huang</style></author><author><style face="normal" font="default" size="100%">Yan Chen</style></author><author><style face="normal" font="default" size="100%">Javier Arroyo</style></author><author><style face="normal" font="default" size="100%">Kyle Benne</style></author><author><style face="normal" font="default" size="100%">Yanfei Li</style></author><author><style face="normal" font="default" size="100%">Valentin Gavan</style></author><author><style face="normal" font="default" size="100%">Lisa Rivalin</style></author><author><style face="normal" font="default" size="100%">Lieve Helsen</style></author><author><style face="normal" font="default" size="100%">Draguna Vrabie</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Marina Sofos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">IBPSA Building Simulation 2019</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">benchmarking</style></keyword><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Model predictive control</style></keyword><keyword><style  face="normal" font="default" size="100%">software development</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><pub-location><style face="normal" font="default" size="100%">Rome, Italy</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Jared Langevin</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building Simulation: Ten Challenges</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">building life cycle</style></keyword><keyword><style  face="normal" font="default" size="100%">building performance simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">zero-net-energy buildings</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Buildings consume more than one-third of the world’s primary energy. Reducing energy use and greenhouse-gas emissions in the buildings sector through energy conservation and efficiency improvements constitutes a key strategy for achieving global energy and environmental goals. Building performance simulation has been increasingly used as a tool for designing, operating and retrofitting buildings to save energy and utility costs. However, opportunities remain for researchers, software developers, practitioners and policymakers to maximize the value of building performance simulation in the design and operation of low energy buildings and communities that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a large scale. This paper presents ten challenges that highlight some of the most important issues in building performance simulation, covering the full building life cycle and a wide range of modeling scales. The formulation and discussion of each challenge aims to provide insights into the state-of-the-art and future research opportunities for each topic, and to inspire new questions from young researchers in this field.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xin Zhou</style></author><author><style face="normal" font="default" size="100%">Da Yan</style></author><author><style face="normal" font="default" size="100%">Jingjing An</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xing Shi</style></author><author><style face="normal" font="default" size="100%">Xing Jin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparative Study of Air-Conditioning Energy Use of Four Office Buildings in China and USA</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building envelope</style></keyword><keyword><style  face="normal" font="default" size="100%">climate</style></keyword><keyword><style  face="normal" font="default" size="100%">energy consumption</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">office buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">technological choice</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">169</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. This study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. Overall, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fisayo Caleb Sangogboye</style></author><author><style face="normal" font="default" size="100%">Ruoxi Jia</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Costas Spanos</style></author><author><style face="normal" font="default" size="100%">Mikkel Baun Kjærgaard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Transactions on Sensor Networks</style></secondary-title><short-title><style face="normal" font="default" size="100%">ACM Trans. Sen. Netw.TOSN</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cyber physical systems</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">k-anonymity</style></keyword><keyword><style  face="normal" font="default" size="100%">Privacy preservation</style></keyword><keyword><style  face="normal" font="default" size="100%">Smart buildings</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2018</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">1 - 22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3-4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Yoshinori Yura</style></author><author><style face="normal" font="default" size="100%">Ryohei Hinokuma</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Novel Variable Refrigerant Flow (VRF) Heat Recovery System Model: Development and Validation</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building performance simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">controls</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">heat recovery</style></keyword><keyword><style  face="normal" font="default" size="100%">validation</style></keyword><keyword><style  face="normal" font="default" size="100%">Variable refrigerant flow</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">168</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;As one of the latest emerging HVAC technologies, the Variable Refrigerant Flow (VRF) system with heat recovery (HR) configurations has obtained extensive attention from both the academia and industry. Compared with the conventional VRF systems with heat pump (HP) configurations, VRF-HR is capable of recovering heat from cooling zones to heating zones and providing simultaneous cooling and heating operations. This can further lead to substantial energy saving potential and more flexible zonal control. In this paper, a novel model is developed to simulate the energy performance of VRF-HR systems. It adheres to a more physics-based development with the ability to simulate the refrigerant loop performance and consider the dynamics of more operational parameters, which is essential for representing more advanced control logics. Another key feature of the model is the introduction of component-level curves for indoor units and outdoor units instead of overall performance curves for the entire system, and thus it requires much fewer user-specified performance curves as model inputs. The validation study shows good agreements between the simulated energy use from the new VRF-HR model and the laboratory measurement data across all operational modes at sub-hourly time steps. The model has been adopted in the official release of the EnergyPlus simulation program since Version 8.6, which enables more accurate and robust assessments of VRF-HR systems to support their applications in energy retrofit of existing buildings or design of zero-net-energy buildings.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cynthia Regnier</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantifying the benefits of a building retrofit using an integrated system approach: A case study</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building retrofit</style></keyword><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy conservation measures</style></keyword><keyword><style  face="normal" font="default" size="100%">energy savings</style></keyword><keyword><style  face="normal" font="default" size="100%">integrated design</style></keyword><keyword><style  face="normal" font="default" size="100%">integrated system</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">159</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Building retrofits provide a large opportunity to significantly reduce energy consumption in the buildings sector. Traditional building retrofits focus on equipment upgrades, often at the end of equipment life or failure, and result in replacement with marginally improved similar technology and limited energy savings. The Integrated System (IS) retrofit approach enables much greater energy savings by leveraging interactive effects between end use systems, enabling downsized or lower energy technologies. This paper presents a case study in Hawaii quantifying the benefits of an IS retrofit approach compared to two traditional retrofit approaches: a Standard Practice of upgrading equipment to meet minimum code requirements, and an Improved Practice of upgrading equipment to a higher efficiency. The IS approach showed an energy savings of 84% over existing building energy use, much higher than the traditional approaches of 13% and 33%. The IS retrofit also demonstrated the greatest energy cost savings potential. While the degree of savings realized from the IS approach will vary by building and climate, these findings indicate that savings on the order of 50% and greater are not possible without an IS approach. It is therefore recommended that the IS approach be universally adopted to achieve deep energy savings.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rajeev Jain</style></author><author><style face="normal" font="default" size="100%">Xuan Luo</style></author><author><style face="normal" font="default" size="100%">Gökhan Sever</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Charlie Catlett</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Representation and evolution of urban weather boundary conditions in downtown Chicago</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Building Performance Simulation</style></secondary-title><short-title><style face="normal" font="default" size="100%">Journal of Building Performance Simulation</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">coupling</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">Urban climate modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">WRF</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/full/10.1080/19401493.2018.1534275https://www.tandfonline.com/doi/pdf/10.1080/19401493.2018.1534275</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1 - 14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This study presents a novel computing technique for data exchange and coupling between a high-resolution weather simulation model and a building energy model, with a goal of evaluating the impact of urban weather boundary conditions on energy performance of urban buildings. The Weather Research and Forecasting (WRF) model is initialized with the operational High-Resolution Rapid Refresh (HRRR) dataset to provide hourly weather conditions over the Chicago region. We utilize the building footprint, land use, and building stock datasets to generate building energy models using EnergyPlus. We mapped the building exterior surfaces to local air nodes to import simulated microclimate data and to export buildings&#039; heat emissions to their local environment. Preliminary experiments for a test area in Chicago show that predicted building cooling energy use differs by about 4.7% for the selected date when compared with simulations using TMY weather data and without considering the urban microclimate boundary conditions.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brian Tarroja</style></author><author><style face="normal" font="default" size="100%">Felicia Chiang</style></author><author><style face="normal" font="default" size="100%">Amir AghaKouchak</style></author><author><style face="normal" font="default" size="100%">Scott Samuelsen</style></author><author><style face="normal" font="default" size="100%">Shuba V. Raghavan</style></author><author><style face="normal" font="default" size="100%">Max Wei</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title><short-title><style face="normal" font="default" size="100%">Applied Energy</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building Energy Demand</style></keyword><keyword><style  face="normal" font="default" size="100%">Climate Change Impacts</style></keyword><keyword><style  face="normal" font="default" size="100%">electric grid</style></keyword><keyword><style  face="normal" font="default" size="100%">Heating Electrification Effects</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0306261918306962https://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/plain</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">225</style></volume><pages><style face="normal" font="default" size="100%">522 - 534</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Climate change and increased electrification of space and water heating in buildings can significantly affect future electricity demand and hourly demand profiles, which has implications for electric grid greenhouse gas emissions and capacity requirements. We use EnergyPlus to quantify building energy demand under historical and under several climate change projections of 32 kinds of building prototypes in 16 different climate zones of California and imposed these impacts on a year 2050 electric grid configuration by simulation in the Holistic Grid Resource Integration and Deployment (HIGRID) model. We find that climate change only prompted modest increases in grid resource capacity and negligible difference in greenhouse gas emissions since the additional electric load generally occurred during times with available renewable generation. Heating electrification, however, prompted a 30–40% reduction in greenhouse gas emissions but required significant grid resource capacity increases, due to the higher magnitude of load increases and lack of readily available renewable generation during the times when electrified heating loads occurred. Overall, this study translates climate change and electrification impacts to system-wide endpoint impacts on future electric grid configurations and highlights the complexities associated with translating building-level impacts to electric system-wide impacts.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Blum</style></author><author><style face="normal" font="default" size="100%">Guanjing Lin</style></author><author><style face="normal" font="default" size="100%">Michael Spears</style></author><author><style face="normal" font="default" size="100%">Janie Page</style></author><author><style face="normal" font="default" size="100%">Jessica Granderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When Data Analytics Meet Site Operation: Benefits and Challenges</style></title><secondary-title><style face="normal" font="default" size="100%">2018 ACEEE Summer Study on Energy Efficiency in Buildings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2018</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Demand for using data analytics for energy management in buildings is rising. Such analytics are required for advanced measurement and verification, commissioning, automated fault-detection and diagnosis, and optimal control. While novel analytics algorithms continue to be developed, bottlenecks and challenges arise when deploying them for demonstration, for a number of reasons that do not necessarily have to do with the algorithms themselves. It is important for developers of new technologies to be aware of the challenges and potential solutions during demonstration. Therefore, this paper describes a recent deployment of an automated, physical model-based, FDD and optimal control tool, highlighting its design and as-operated benefits that the tool provides. Furthermore, the paper presents challenges faced during deployment and testing along with solutions used to overcome these challenges. The challenges have been grouped into four categories: Data Management, Physical Model Development and Integration, Software Development and Deployment, and Operator Use. The paper concludes by discussing how challenges with this project generalize to common cases, how they could compare to other projects in their severity, and how they may be addressed.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brahm van der Heijde</style></author><author><style face="normal" font="default" size="100%">Marcus Fuchs</style></author><author><style face="normal" font="default" size="100%">Carles Ribas Tugores</style></author><author><style face="normal" font="default" size="100%">Gerald Schweiger</style></author><author><style face="normal" font="default" size="100%">Kevin Sartor</style></author><author><style face="normal" font="default" size="100%">Daniele Basciotti</style></author><author><style face="normal" font="default" size="100%">Dirk Muller</style></author><author><style face="normal" font="default" size="100%">Christoph Nytsch-Geusen</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Lieve Helsen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Conversion and Management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">151</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">2001049</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Quantifying the Impact of Occupant Behavior on Energy Savings of Energy Conservation Measures</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To improve energy efficiency—during new buildings design or during a building retrofit—evaluating the energy savings potential of energy conservation measures (ECMs) is a critical task. In building retrofits, occupant behavior significantly impacts building energy use and is a leading factor in uncertainty when determining the effectiveness of retrofit ECMs. Current simulation-based assessment methods simplify the representation of occupant behavior by using a standard or representative set of static and homogeneous assumptions ignoring the dynamics, stochastics, and diversity of occupant&#039;s energy-related behavior in buildings. The simplification contributes to significant gaps between the simulated and measured actual energy performance of buildings.&lt;/p&gt;&lt;p&gt;This study presents a framework for quantifying the impact of occupant behaviors on ECM energy savings using building performance simulation. During the first step of the study, three occupant behavior styles (austerity, normal, and wasteful) were defined to represent different levels of energy consciousness of occupants regarding their interactions with building energy systems (HVAC, windows, lights and plug-in equipment). Next, a simulation workflow was introduced to determine a range of the ECM energy savings. Then, guidance was provided to interpret the range of ECM savings to support ECM decision making. Finally, a pilot study was performed in a real building to demonstrate the application of the framework. Simulation results show that the impact of occupant behaviors on ECM savings vary with the type of ECM. Occupant behavior minimally affects energy savings for ECMs that are technology-driven (the relative savings differ by less than 2%) and have little interaction with the occupants; for ECMs with strong occupant interaction, such as the use of zonal control variable refrigerant flow system and natural ventilation, energy savings are significantly affected by occupant behavior (the relative savings differ by up to 20%). The study framework provides a novel, holistic approach to assessing the uncertainty of ECM energy savings related to occupant behavior, enabling stakeholders to understand and assess the risk of adopting energy efficiency technologies for new and existing buildings.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jingjing An</style></author><author><style face="normal" font="default" size="100%">Da Yan</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Novel Stochastic Modeling Method to Simulate Cooling Loads in Residential Districts</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized;this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2)occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution,and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">William J. Fisk</style></author><author><style face="normal" font="default" size="100%">Norman Bourassa</style></author><author><style face="normal" font="default" size="100%">Wanyu R. Chan</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">H.Y. Iris Cheung</style></author><author><style face="normal" font="default" size="100%">Toshifumi Hotchi</style></author><author><style face="normal" font="default" size="100%">Margarita Kloss</style></author><author><style face="normal" font="default" size="100%">Sang Hoon Lee</style></author><author><style face="normal" font="default" size="100%">Phillip N. Price</style></author><author><style face="normal" font="default" size="100%">Oren Schetrit</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Sarah C. Taylor-Lange</style></author><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Small and Medium Building Efficiency Toolkit and Community Demonstration Program</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CBES</style></keyword><keyword><style  face="normal" font="default" size="100%">commercial buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energy savings</style></keyword><keyword><style  face="normal" font="default" size="100%">indoor air quality</style></keyword><keyword><style  face="normal" font="default" size="100%">indoor environmental quality</style></keyword><keyword><style  face="normal" font="default" size="100%">outdoor air measurement technology</style></keyword><keyword><style  face="normal" font="default" size="100%">outdoor airflow intake rate</style></keyword><keyword><style  face="normal" font="default" size="100%">retrofit</style></keyword><keyword><style  face="normal" font="default" size="100%">ventilation rate</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2017</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Small commercial buildings in the United States consume 47 percent of all primary energy consumed in the building sector. Retrofitting small and medium commercial buildings may pose a steep challenge for owners, as many lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. To address this problem, this project developed the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit that calculates the energy use of a building, identifies and evaluates retrofit measures based on energy savings, energy cost savings, and payback. The CBES Toolkit includes a web app for end users and the CBES Application Programming Interface for integrating CBES with other energy software tools. The toolkit provides a rich feature set, including the following:&lt;/p&gt;&lt;ol&gt;&lt;li&gt;Energy Benchmarking providing an Energy Star score&lt;/li&gt;&lt;li&gt;Load Shape Analysis to identify potential building operation improvements&lt;/li&gt;&lt;li&gt;Preliminary Retrofit Analysis which uses a custom developed pre-simulated database&lt;/li&gt;&lt;li&gt;Detailed Retrofit Analysis which utilizes real time EnergyPlus simulations&lt;/li&gt;&lt;/ol&gt;&lt;p&gt;In a parallel effort the project team developed technologies to measure outdoor airflow rate; commercialization and use would avoid both excess energy use from over ventilation and poor indoor air quality resulting from under ventilation.&lt;/p&gt;&lt;p&gt;If CBES is adopted by California’s statewide small office and retail buildings, by 2030 the state can anticipate 1,587 gigawatt hours of electricity savings, 356 megawatts of non-coincident peak demand savings, 30.2 megatherms of natural gas savings, $227 million of energy-related cost savings, and reduction of emissions by 757,866 metric tons of carbon dioxide equivalent. In addition, consultant costs will be reduced in the retrofit analysis process.&lt;/p&gt;&lt;p&gt;CBES contributes to the energy savings retrofit field by enabling a straightforward and uncomplicated decision-making process for small and medium business owners and leveraging different levels of assessment to match user background, preference, and data availability.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-2001054</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Qi Zhang</style></author><author><style face="normal" font="default" size="100%">Da Yan</style></author><author><style face="normal" font="default" size="100%">Jingjing An</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Wei Tian</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">air handling unit</style></keyword><keyword><style  face="normal" font="default" size="100%">chiller plant</style></keyword><keyword><style  face="normal" font="default" size="100%">equipment sizing</style></keyword><keyword><style  face="normal" font="default" size="100%">internal heat gain</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial diversity</style></keyword><keyword><style  face="normal" font="default" size="100%">stochastic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Internal heat gains from occupants, lighting, and plug loads are significant components of the space cooling load in an office building. Internal heat gains vary with time and space. The spatial diversity is significant, even for spaces with the same function in the same building. The stochastic nature of internal heat gains makes determining the peak cooling load to size air-conditioning systems a challenge. The traditional conservative practice of considering the largest internal heat gain among spaces and applying safety factors overestimates the space cooling load, which leads to oversized air-conditioning equipment and chiller plants. In this study, a field investigation of several large office buildings in China led to the development of a new probabilistic approach that represents the spatial diversity of the design internal heat gain of each tenant as a probability distribution function. In a large office building, a central chiller plant serves all air handling units (AHUs), with each AHU serving one or more floors of the building. Therefore, the spatial diversity should be considered differently when the peak cooling loads to size the AHUs and chillers are calculated. The proposed approach considers two different levels of internal heat gains to calculate the peak cooling loads and size the AHUs and chillers in order to avoid oversizing, improve the overall operating efficiency, and thus reduce energy use.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xinqiao Yu</style></author><author><style face="normal" font="default" size="100%">Da Yan</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Dandan Zhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study on Energy Performance of Variable Refrigerant Flow Systems and Variable Air Volume Systems in Office Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">comparative analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">energy performance</style></keyword><keyword><style  face="normal" font="default" size="100%">field measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">Variable Air Volume (VAV) Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Variable Refrigerant Flow (VRF) Systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Variable air volume (VAV) systems and variable refrigerant flow (VRF) systems are popularly used in office buildings. This study investigated VAV and VRF systems in five typical office buildings in China, and compared their air conditioning energy use. Site survey and field measurements were conducted to collect data of building characteristics and operation. Measured cooling electricity use was collected from sub-metering in the five buildings. The sub-metering data, normalized by climate and operating hours, show that VRF systems consumed much less air conditioning energy by up to 70% than VAV systems. This is mainly due to the different operation modes of both system types leading to much fewer operating hours of the VRF systems. Building simulation was used to quantify the impact of operation modes of VRF and VAV systems on cooling loads using a prototype office building in China. Simulated results show the VRF operation mode leads to much less cooling loads than the VAV operation mode, by 42% in Hong Kong and 53% in Qingdao. The VRF systems operated in the part-time-part-space mode enabling occupants to turn on air-conditioning only when needed and when spaces were occupied, while the VAV systems operated in the full-time-full-space mode limiting occupants’ control of operation. The findings provide insights into VRF systems operation and controls as well as its energy performance, which can inform HVAC designers on system selection and building operators or facility managers on improving VRF system operations.&amp;nbsp; &amp;nbsp;&amp;nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;br /&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xin Liang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Geoffrey Qiping Shen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving the accuracy of energy baseline models for commercial buildings with occupancy data</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">baseline model</style></keyword><keyword><style  face="normal" font="default" size="100%">building energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy Efficiency Retrofit</style></keyword><keyword><style  face="normal" font="default" size="100%">Measurement and verification</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;More than 80% of energy is consumed during operation phase of a building’s life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&amp;amp;V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&amp;amp;V and facilitate investment strategies of energy efficiency retrofit.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xin Liang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Geoffrey Qiping Shen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Occupancy data analytics and prediction: a case study</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant presence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Simulation Approach to Estimate Energy Savings Potential of Occupant Behavior Measures</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">behavior measure</style></keyword><keyword><style  face="normal" font="default" size="100%">Behavior Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">building performance simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy savings</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupant behavior in buildings is a leading factor influencing energy use in buildings. Low-cost behavioral solutions have demonstrated significant potential energy savings. Estimating the behavioral savings potential is important for a more effective design of behavior change interventions, which in turn will support more effective energy-efficiency policies. This study introduces a simulation approach to estimate the energy savings potential of occupant behavior measures. First it defines five typical occupant behavior measures in office buildings, then simulates and analyzes their individual and integrated impact on energy use in buildings. The energy performance of the five behavior measures was evaluated using EnergyPlus simulation for a real office building across four typical U.S. climates and two vintages. The Occupancy Simulator was used to simulate the occupant movement in each zone with inputs from the site survey of the case building. Based on the simulation results, the occupant behavior measures can achieve overall site energy savings as high as 22.9% for individual measures and up to 41.0% for integrated measures. Although energy savings of behavior measures would vary depending upon many factors, the presented simulation approach is robust and can be adopted for other studies aiming to quantify occupant behavior impact on building performance.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sang Hoon Lee</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Geof Sawaya</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Sarah C. Taylor-Lange</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance</style></title><secondary-title><style face="normal" font="default" size="100%">Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy conservation measure</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">High Performance computing</style></keyword><keyword><style  face="normal" font="default" size="100%">retrofit</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2015</style></date></pub-dates></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Small and medium-sized commercial buildings can be retrofitted to significantly reduce their energy use, however it is a huge challenge as owners usually lack of the expertise and resources to conduct detailed on-site energy audit to identify and evaluate cost-effective energy technologies. This study presents a DEEP (database of energy efficiency performance) that provides a direct resource for quick retrofit analysis of commercial buildings. DEEP, compiled from the results of about ten million EnergyPlus simulations, enables an easy screening of ECMs (energy conservation measures) and retrofit analysis. The simulations utilize prototype models representative of small and mid-size offices and retails in California climates. In the formulation of DEEP, large scale EnergyPlus simulations were conducted on high performance computing clusters to evaluate hundreds of individual and packaged ECMs covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and service hot water. The architecture and simulation environment to create DEEP is flexible and can expand to cover additional building types, additional climates, and new ECMs. In this study DEEP is integrated into a web-based retrofit toolkit, the Commercial Building Energy Saver, which provides a platform for energy retrofit decision making by querying DEEP and unearthing recommended ECMs, their estimated energy savings and financial payback.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004494</style></custom2><section><style face="normal" font="default" size="100%">738</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reshma Singh</style></author><author><style face="normal" font="default" size="100%">Baptiste Ravache</style></author><author><style face="normal" font="default" size="100%">Spencer M. Dutton</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CLIMATE-SPECIFIC MODELING AND ANALYSIS FOR BEST-PRACTICE INDIAN OFFICE BUILDINGS</style></title><secondary-title><style face="normal" font="default" size="100%">BS2015: 14th Conference of International Building Performance Simulation Association</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Climate specific building energy models</style></keyword><keyword><style  face="normal" font="default" size="100%">india</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ibpsa.org/proceedings/BS2015/p2714.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Hyderabad, India</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper describes the methodology and results of building energy modeling to validate and quantify the energy savings from conservation measures in medium-sized office buildings in four different climate zones in India. We present the different energy measures and their expected and simulated performances and discuss the results and the influence of climate.&amp;nbsp;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Sang Hoon Lee</style></author><author><style face="normal" font="default" size="100%">Sarah C. Taylor-Lange</style></author><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Phillip N. Price</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Commercial Building Energy Saver: An energy retrofit analysis toolkit</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building Technologies Department</style></keyword><keyword><style  face="normal" font="default" size="100%">Building Technology and Urban Systems Division</style></keyword><keyword><style  face="normal" font="default" size="100%">buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">buildings energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">Commercial Building Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">conservation measures</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">External</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrofit Energy</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation research</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">9/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">159</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Small commercial buildings in the United States consume 47% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004502</style></custom2><section><style face="normal" font="default" size="100%">298</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sang Hoon Lee</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Geof Sawaya</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents a method and process to establish a database of energy efficiency performance (DEEP) to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 35 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones. DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and domestic hot water. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of an on-going project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users’ decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit. DEEP will be migrated into the DEnCity - DOE’s Energy City, which integrates large-scale energy data for multi-purpose, open, and dynamic database leveraging diverse source of existing simulation data.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-180309</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author><author><style face="normal" font="default" size="100%">Ryohei Hinokuma</style></author><author><style face="normal" font="default" size="100%">Shinichi Kasahara</style></author><author><style face="normal" font="default" size="100%">Yoshinori Yura</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development and validation of a new variable refrigerant flow systemmodel in EnergyPlus</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">Heat pump</style></keyword><keyword><style  face="normal" font="default" size="100%">model validation</style></keyword><keyword><style  face="normal" font="default" size="100%">Variable refrigerant flow</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">9/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">117</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Variable refrigerant flow (VRF) systems vary the refrigerant flow to meet the dynamic zone thermalloads, leading to more efficient operations than other system types. This paper introduces a new modelthat simulates the energy performance of VRF systems in the heat pump (HP) operation mode. Com-pared with the current VRF-HP models implemented in EnergyPlus, the new VRF system model has morecomponent models based on physics and thus has significant innovations in: (1) enabling advanced con-trols, including variable evaporating and condensing temperatures in the indoor and outdoor units, andvariable fan speeds based on the temperature and zone load in the indoor units, (2) adding a detailedrefrigerant pipe heat loss calculation using refrigerant flow rate, operational conditions, pipe length, andpipe insulation materials, (3) improving accuracy of simulation especially in partial load conditions, and(4) improving the usability of the model by significantly reducing the number of user input performancecurves. The VRF-HP model is implemented in EnergyPlus and validated with measured data from fieldtests. Results show that the new VRF-HP model provides more accurate estimate of the VRF-HP systemperformance, which is key to determining code compliance credits as well as utilities incentive for VRFtechnologies.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004499</style></custom2><section><style face="normal" font="default" size="100%">399</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>46</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Evan Mills</style></author><author><style face="normal" font="default" size="100%">Jessica Granderson</style></author><author><style face="normal" font="default" size="100%">Wanyu R. Chan</style></author><author><style face="normal" font="default" size="100%">Richard C. Diamond</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Bruce Nordman</style></author><author><style face="normal" font="default" size="100%">Paul A. Mathew</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Gerald Robinson</style></author><author><style face="normal" font="default" size="100%">Stephen E. Selkowitz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Green, Clean, &amp; Mean: Pushing the Energy Envelope in Tech Industry Buildings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Lawrence Berkeley National Laboratory</style></publisher><abstract><style face="normal" font="default" size="100%">&lt;p&gt;When it comes to innovation in energy and building performance, one can expect leading-edge activity from the technology sector. As front-line innovators in design, materials science, and information management, developing and operating high-performance buildings is a natural extension of their core business.&lt;/p&gt;&lt;p&gt;The energy choices made by technology companies have broad importance given their influence on society at large as well as the extent of their own energy footprint. Microsoft, for example, has approximately 250 facilities around the world (30 million square feet of floor area), with significant aggregate energy use of approximately 4 million kilowatt-hours per day.&lt;/p&gt;&lt;p&gt;There is a degree of existing documentation of efforts to design, build, and operate facilities in the technology sector. However, the material is fragmented and typically looks only at a single company, or discrete projects within a company.Yet, there is no single resource for corporate planners and decision makers that takes stock of the opportunities and documents sector-specific case studies in a structured manner. This report seeks to fill that gap, doing so through a combination of generalized technology assessments (“Key Strategies”) and case studies (“Flagship Projects”).&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1005070E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Sarah C. Taylor-Lange</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A pattern-based automated approach to building energy model calibration</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Building model calibration is critical in bringing simulated energy use closer to the actual consumption. This paper presents a novel, automated model calibration approach that uses logic linking parameter tuning with bias pattern recognition to overcome some of the disadvantages associated with traditional calibration processes. The pattern-based process contains four key steps: (1) running the original precalibrated energy model to obtain monthly simulated electricity and gas use; (2) establishing a pattern bias, either Universal or Seasonal Bias, by comparing load shape patterns of simulated and actual monthly energy use; (3) using programmed logic to select which parameter to tune first based on bias pattern, weather and input parameter interactions; and (4) automatically tuning the calibration parameters and checking the progress using pattern-fit criteria. The automated calibration algorithm was implemented in the Commercial Building Energy Saver, a web-based building energy retrofit analysis toolkit. The proof of success of the methodology was demonstrated using a case study of an office building located in San Francisco. The case study inputs included the monthly electricity bill, monthly gas bill, original building model and weather data with outputs resulting in a calibrated model that more closely matched that of the actual building energy use profile. The novelty of the developed calibration methodology lies in linking parameter tuning with the underlying logic associated with bias pattern identification. Although there are some limitations to this approach, the pattern-based automated calibration methodology can be universally adopted as an alternative to manual or hierarchical calibration approaches.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004495</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jianjun Xia</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Qi Shen</style></author><author><style face="normal" font="default" size="100%">Wei Feng</style></author><author><style face="normal" font="default" size="100%">Le Yang</style></author><author><style face="normal" font="default" size="100%">Piljae Im</style></author><author><style face="normal" font="default" size="100%">Alison Lu</style></author><author><style face="normal" font="default" size="100%">Mahabir Bhandari</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of Building Energy Use Data Between the United States and China</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">comparison</style></keyword><keyword><style  face="normal" font="default" size="100%">data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">data model</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy benchmarking</style></keyword><keyword><style  face="normal" font="default" size="100%">energy monitoring system</style></keyword><keyword><style  face="normal" font="default" size="100%">energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">retrofit</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">165-175</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Buildings in the United States and China consumed 41% and 28% of the total primary energy in 2011, respectively. Good energy data are the cornerstone to understanding building energy performance and supporting research, design, operation, and policy making for low energy buildings. This paper presents initial outcomes from a joint research project under the U.S.–China Clean Energy Research Center for Building Energy Efficiency. The goal is to decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders. This paper first reviews and compares several popular existing building energy monitoring systems in both countries. Next a standard energy data model is presented. A detailed, measured building energy data comparison was conducted for a few office buildings in both countries. Finally issues of data collection, quality, sharing, and analysis methods are discussed. It was found that buildings in both countries performed very differently, had potential for deep energy retrofit, but that different efficiency measures should apply.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-6669E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Cheng Li</style></author><author><style face="normal" font="default" size="100%">Richard C. Diamond</style></author><author><style face="normal" font="default" size="100%">Da Yan</style></author><author><style face="normal" font="default" size="100%">Qi Zhang</style></author><author><style face="normal" font="default" size="100%">Xin Zhou</style></author><author><style face="normal" font="default" size="100%">Siyue Guo</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Jingyi Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrated Design for High Performance Buildings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><custom2><style face="normal" font="default" size="100%">LBNL-6991E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</style></author><author><style face="normal" font="default" size="100%">Oren Schetrit</style></author><author><style face="normal" font="default" size="100%">Liping Wang</style></author><author><style face="normal" font="default" size="100%">Shinichi Kasahara</style></author><author><style face="normal" font="default" size="100%">Yoshinori Yura</style></author><author><style face="normal" font="default" size="100%">Ryohei Hinokuma</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Model to Simulate Energy Performance of VRF Systems</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents a new model to simulate energy performance of variable refrigerant flow (VRF) systems in heat pump operation mode (either cooling or heating is provided but not simultaneously). The main improvement of the new model is the introduction of the evaporating and condensing temperature in the indoor and outdoor unit capacity modifier functions. The independent variables in the capacity modifier functions of the existing VRF model in EnergyPlus are mainly room wet-bulb temperature and outdoor dry-bulb temperature in cooling mode and room dry-bulb temperature and outdoor wet-bulb temperature in heating mode. The new approach allows compliance with different specifications of each indoor unit so that the modeling accuracy is improved. The new VRF model was implemented in a custom version of EnergyPlus 7.2. This paper first describes the algorithm for the new VRF model, which is then used to simulate the energy performance of a VRF system in a Prototype House in California that complies with the requirements of Title 24 – the California Building Energy Efficiency Standards. The VRF system performance is then compared with three other types of HVAC systems: the Title 24-2005 Baseline system, the traditional High Efficiency system, and the EnergyStar Heat Pump system in three typical California climates: Sunnyvale, Pasadena and Fresno. Calculated energy savings from the VRF systems are significant. The HVAC site energy savings range from 51 to 85%, while the TDV (Time Dependent Valuation) energy savings range from 31 to 66% compared to the Title 24 Baseline Systems across the three climates. The largest energy savings are in Fresno climate followed by Sunnyvale and Pasadena. The paper discusses various characteristics of the VRF systems contributing to the energy savings. It should be noted that these savings are calculated using the Title 24 prototype House D under standard operating conditions. Actual performance of the VRF systems for real houses under real operating conditions will vary.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-6666E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kristen Parrish</style></author><author><style face="normal" font="default" size="100%">Reshma Singh</style></author><author><style face="normal" font="default" size="100%">Szu-Cheng Chien</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Role of International Partnerships in Delivering Low- Energy Building Design: A Case Study of the Singapore Scientific Planning Process</style></title><secondary-title><style face="normal" font="default" size="100%">Sustainable Cities and Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper explores the role of international partnerships to facilitate low-energy building&lt;br /&gt;design, construction, and operations. We briefly discuss multiple collaboration models&lt;br /&gt;and the levels of impact they support. We present a case study of one collaborative&lt;br /&gt;partnership model, the Scientific Planning Support (SPS) team. Staff from the Lawrence&lt;br /&gt;Berkeley National Laboratory, the Austrian Institute of Technology, and Nanyang&lt;br /&gt;Technological University formed the SPS team to provide design assistance and process&lt;br /&gt;support during the design phase of a low-energy building project. Specifically, the SPS&lt;br /&gt;team worked on the Clean Tech Two project, a tenanted laboratory and office building&lt;br /&gt;that seeks Green Mark Platinum, the highest green building certification in Singapore.&lt;br /&gt;The SPS team hosted design charrettes, helped to develop design alternatives, and&lt;br /&gt;provided suggestions on the design process in support of this aggressive energy target.&lt;br /&gt;This paper describes these efforts and discusses how teams like the SPS team and other&amp;nbsp;&lt;span style=&quot;font-size: 13.008px;&quot;&gt;partnership schemes can be leveraged to achieve high performance, low-energy buildings&lt;/span&gt;&lt;span style=&quot;font-size: 13.008px;&quot;&gt;at an international scale.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Siyue Guo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">model calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">overtime occupancy</style></keyword><keyword><style  face="normal" font="default" size="100%">stochastic modeling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-6670E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Wei Feng</style></author><author><style face="normal" font="default" size="100%">Alison Lu</style></author><author><style face="normal" font="default" size="100%">Jianjun Xia</style></author><author><style face="normal" font="default" size="100%">Le Yang</style></author><author><style face="normal" font="default" size="100%">Qi Shen</style></author><author><style face="normal" font="default" size="100%">Piljae Im</style></author><author><style face="normal" font="default" size="100%">Mahabir Bhandari</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building Energy Monitoring and Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2013</style></date></pub-dates></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;U.S. and China are the world&#039;s top two economics. Together they consumed one-third of the world&#039;s primary energy. It is an unprecedented opportunity and challenge for governments, researchers and industries in both countries to join together to address energy issues and global climate change. Such joint collaboration has huge potential in creating new jobs in energy technologies and services.&lt;/p&gt;&lt;p&gt;Buildings in the US and China consumed about 40% and 25% of the primary energy in both countries in 2010 respectively. Worldwide, the building sector is the largest contributor to the greenhouse gas emission. Better understanding and improving the energy performance of buildings is a critical step towards sustainable development and mitigation of global climate change.&lt;/p&gt;&lt;p&gt;This project aimed to develop a standard methodology for building energy data definition, collection, presentation, and analysis; apply the developed methods to a standardized energy monitoring platform, including hardware and software, to collect and analyze building energy use data; and compile offline statistical data and online real-time data in both countries for fully understanding the current status of building energy use. This helps decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders.&lt;/p&gt;&lt;p&gt;Key research findings were summarized as follows:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Identified the need for a standard data model and platform to collect, process, analyze, and exchange building performance data due to different definitions of energy use and boundary, difficulty in exchanging data, and lack of current standards.&lt;/li&gt;&lt;li&gt;Compared energy monitoring systems to identify gaps, including iSagy, Pulse Energy, SkySpark, sMap, EPP, ION, and Metasys.&lt;/li&gt;&lt;li&gt;Contributed to develop a standard data model to represent energy use in buildings (ISO standard 12655 and a Chinese national standard)&lt;/li&gt;&lt;li&gt;Determined that buildings in the United States and China are very different in design, operation, maintenance, occupant behavior: U.S. buildings have more stringent comfort standards regarding temperature, ventilation, lighting, and hot-water use and therefore higher internal loads and operating hours, and China buildings having higher lighting energy use, seasonal HVAC operation, more operators, more use of natural ventilation, less outdoor ventilation air, and wider range of comfort temperature.&lt;/li&gt;&lt;li&gt;Completed data collection for six office buildings, one in UC Merced campus, one in Sacramento, one in Berkeley, one in George Tech campus, and two in Beijing.&lt;/li&gt;&lt;li&gt;Compiled a source book of 10 selected buildings in the United States and China with detailed descriptions of the buildings, data points, and monitoring systems, and containing energy analysis of each building and an energy benchmarking among all buildings.&lt;/li&gt;&lt;li&gt;Recognized limited availability of quality data, particularly with long periods of time-interval data, and general lack of value for good data and large datasets.&lt;/li&gt;&lt;li&gt;Compiled a building energy database, with detailed energy end use at 1-hour or 15-minute time interval, of six office buildings — four in the U.S. and two in China. The database is available to the public and is a valuable resource for building research.&lt;/li&gt;&lt;li&gt;Developed methods and used them in data analysis of building performance for the five buildings with adequate data, including energy benchmarking, profiling (daily, weekly, monthly), and diagnostics.&lt;/li&gt;&lt;li&gt;Recommended energy efficiency measures for building retrofit in both countries. U.S. buildings show more potential savings by reducing operation time, reducing plug-loads, expanding comfort temperature range, and turning off lights or equipment when not in use; while Chinese buildings can save energy by increasing lighting system efficiency, and improving envelope insulation and HVAC equipment efficiency.&lt;/li&gt;&lt;/ul&gt;&lt;p&gt;The research outputs from the project can help better understand energy performance of buildings, improve building operations to reduce energy waste and increase efficiency, identify retrofit opportunities for existing buildings, and provide guideline to improve the design of new buildings. The standardized energy monitoring and analysis platform as well as the collected real building data can also be used for other CERC projects that need building energy measurements, and be further linked to building energy benchmarking and rating/labeling systems.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-6640E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Spencer M. Dutton</style></author><author><style face="normal" font="default" size="100%">Hui Zhang</style></author><author><style face="normal" font="default" size="100%">Yongchao Zhai</style></author><author><style face="normal" font="default" size="100%">Edward A. Arens</style></author><author><style face="normal" font="default" size="100%">Youness Bennani Smires</style></author><author><style face="normal" font="default" size="100%">Samuel L. Brunswick</style></author><author><style face="normal" font="default" size="100%">Kyle S. Konis</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of a stochastic window use model in EnergyPlus</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2012, 5th National Conference of IBPSA-USA, August 1-3, 2012</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://escholarship.org/uc/item/2gm7r783</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Madison, WI</style></pub-location><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Natural ventilation, used appropriately, has the potential to provide both significant HVAC energy savings, and improvements in occupant satisfaction.&lt;/p&gt;&lt;p&gt;Central to the development of natural ventilation models is the need to accurately represent the behavior of building occupants. The work covered in this paper describes a method of implementing a stochastic window model in EnergyPlus. Simulated window use data from three stochastic window opening models was then compared to measured window opening behavior, collected in a naturally-ventilated office in California. Recommendations regarding the selection of stochastic window use models, and their implementation in EnergyPlus, are presented.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tobias Maile</style></author><author><style face="normal" font="default" size="100%">Mangesh Basarkar</style></author><author><style face="normal" font="default" size="100%">James O&#039;Donnell</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Kevin Settlemyre</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping Hvac Systems for Simulation In EnergyPlus</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2012 IBPSA Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2012</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Madison, WI, USA</style></pub-location><abstract><style face="normal" font="default" size="100%">&lt;p&gt;For building energy simulation tools to be accessible to designers, tool interfaces should present a conventional view of HVAC systems to the user, and then map this view to the internal data model used in the tool. The paper outlines a process that enables design engineers to create HVAC system representations using industry standard terminology and system, icon and typological representations and convert that unified representation into the format required by the whole building energy simulation tool EnergyPlus. This paper describes each stage of the conversion process, which involves transformations between the following representations: 1) engineer&#039;s representation, 2) component connectivity representation, 3) representation in the internal data model used in the Simergy graphical user interface for EnergyPlus, and 4) EnergyPlus representation.&lt;/p&gt;&lt;p&gt;The paper also describes mappings between these representations and the development of a rule-based validation and assignment framework required to implement that mapping. In addition, the paper describes the implementation of this process in Simergy.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-5565E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">K. Nandha Kumar</style></author><author><style face="normal" font="default" size="100%">B. Sivaneasan</style></author><author><style face="normal" font="default" size="100%">P.L. So</style></author><author><style face="normal" font="default" size="100%">H.B. Gooi</style></author><author><style face="normal" font="default" size="100%">Nilesh Jadhav</style></author><author><style face="normal" font="default" size="100%">Reshma Singh</style></author><author><style face="normal" font="default" size="100%">Chris Marnay</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sustainable Campus with PEV and Microgrid</style></title><secondary-title><style face="normal" font="default" size="100%">2012 ACEEE Summer Study on Energy Efficiency in Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">campus</style></keyword><keyword><style  face="normal" font="default" size="100%">electric vehicles</style></keyword><keyword><style  face="normal" font="default" size="100%">energy</style></keyword><keyword><style  face="normal" font="default" size="100%">loads</style></keyword><keyword><style  face="normal" font="default" size="100%">microgrids</style></keyword><keyword><style  face="normal" font="default" size="100%">renewable energy</style></keyword><keyword><style  face="normal" font="default" size="100%">transport</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://aceee.org/files/proceedings/2012/data/papers/0193-000363.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Pacific Grove, CA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Market penetration of electric vehicles (EVs) is gaining momentum, as is the move&lt;br /&gt;towards increasingly distributed, clean and renewable electricity sources. EV charging shifts a&lt;br /&gt;significant portion of transportation energy use onto building electricity meters. Hence,&lt;br /&gt;integration strategies for energy-efficiency in buildings and transport sectors are of increasing&lt;br /&gt;importance. This paper focuses on a portion of that integration: the analysis of an optimal&lt;br /&gt;interaction of EVs with a building-serving transformer, and coupling it to a microgrid that&lt;br /&gt;includes PV, a fuel cell and a natural gas micro-turbine. The test-case is the Nanyang&lt;br /&gt;Technological University (NTU), Singapore campus. The system under study is the Laboratory&lt;br /&gt;of Clean Energy Research (LaCER) Lab that houses the award winning Microgrid Energy&lt;br /&gt;Management System (MG-EMS) project. The paper analyses three different case scenarios to&lt;br /&gt;estimate the number of EVs that can be supported by the building transformer serving LaCER.&lt;br /&gt;An approximation of the actual load data collected for the building into different time intervals is&lt;br /&gt;performed for a transformer loss of life (LOL) calculation. The additional EV loads that can be&lt;br /&gt;supported by the transformer with and without the microgrid are analyzed. The numbers of&lt;br /&gt;possible EVs that can be charged at any given time under the three scenarios are also determined.&lt;br /&gt;The possibility of using EV fleet at NTU campus to achieve demand response capability and&lt;br /&gt;intermittent PV output leveling through vehicle to grid (V2G) technology and building energy&lt;br /&gt;management systems is also explored.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richard See</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Pramod Sreekanathan</style></author><author><style face="normal" font="default" size="100%">Mangesh Basarkar</style></author><author><style face="normal" font="default" size="100%">James O&#039;Donnell</style></author><author><style face="normal" font="default" size="100%">Kevin Settlemyre</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a user interface for the EnergyPlus whole building energy simulation program</style></title><secondary-title><style face="normal" font="default" size="100%">IBPSA Building Simulation 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2011</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">James O&#039;Donnell</style></author><author><style face="normal" font="default" size="100%">Richard See</style></author><author><style face="normal" font="default" size="100%">Cody Rose</style></author><author><style face="normal" font="default" size="100%">Tobias Maile</style></author><author><style face="normal" font="default" size="100%">Vladimir Bazjanac</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SimModel: A domain data model for whole building energy simulation</style></title><secondary-title><style face="normal" font="default" size="100%">IBPSA Building Simulation 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2011</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many inadequacies exist within industry-standard data models as used by present-day whole-building energy simulation software. Tools such as EnergyPlus and DOE-2 use custom schema definitions (IDD and BDL respectively) as opposed to standardized schema definitions (defined in XSD, EXPRESS, etc.). Non-standard data modes lead to a requirement for application developers to develop bespoke interfaces. Such tools have proven to be error prone in their implementation – typically resulting in information loss. &lt;/p&gt;&lt;p&gt;This paper presents a Simulation Domain Model (SimModel) - a new interoperable XML-based data model for the building simulation domain. SimModel provides a consistent data model across all aspects of the building simulation process, thus preventing information loss. The model accounts for new simulation tool architectures, existing and future systems, components and features. In addition, it is a multi-representation model that enables integrated geometric and MEP simulation configuration data. The SimModel objects ontology moves away from tool-specific, non-standard nomenclature by implementing an industry-validated terminology aligned with Industry Foundation Classes (IFC). &lt;/p&gt;&lt;p&gt;The first implementation of SimModel supports translations from IDD, Open Studio IDD, gbXML and IFC. In addition, the EnergyPlus Graphic User Interface (GUI) employs SimModel as its internal data model. Ultimately, SimModel will form the basis for a new IFC Model View Definition (MVD) that will enable data exchange from HVAC Design applications to Energy Analysis applications. Extensions to SimModel could easily support other data formats and simulations (e.g. Radiance, COMFEN, etc.).&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-5566E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Stephen E. Selkowitz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Energy Impact of Window Technologies for Commercial Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">2010 ACEEE Summer Study</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building energy performance</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">shading controls</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">windows</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Trevor Bailey</style></author><author><style face="normal" font="default" size="100%">Zheng O&#039;Neill</style></author><author><style face="normal" font="default" size="100%">Madhusudana Shashanka</style></author><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Continuous Commissioning of Commercial Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">DoD SERDP-ESTCP Partners in Environmental Technology Technical Symposium and Workshop</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2010</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Washington, D.C.</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">LBNL-5734E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Massieh Najafi</style></author><author><style face="normal" font="default" size="100%">David M. Auslander</style></author><author><style face="normal" font="default" size="100%">Peter L. Bartlett</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Michael D. Sohn</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling and Measurement Constraints in Fault Diagnostics for HVAC Systems</style></title><secondary-title><style face="normal" font="default" size="100%">ASME Journal of Dynamic Systems, Measurement, and Controls</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many studies have shown that energy savings of five to fifteen percent are achievable in commercial buildings by detecting and correcting building faults, and optimizing building control systems. However,in spite of good progress in developing tools for determining HVAC diagnostics, methods to detect faults in HVAC systems are still generally undeveloped. Most approaches use numerical filtering or parameter estimation methods to compare data from energy meters and building sensors to predictions from mathematical or statistical models. They are effective when models are relatively accurate and data contain few errors. In this paper, we address the case where models are imperfect and data are variable, uncertain, and can contain error. We apply a Bayesian updating approach that is systematic in managing and accounting for most forms of model and data errors. The proposed method uses both knowledge of first principle modeling and empirical results to analyze the system performance within the boundaries defined by practical constraints. We demonstrate the approach by detecting faults in commercial building air handling units. We find that the limitations that exist in air handling unit diagnostics due to practical constraints can generally be effectively addressed through the proposed approach.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Massieh Najafi</style></author><author><style face="normal" font="default" size="100%">David M. Auslander</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Michael D. Sohn</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Statistical Pattern Analysis Framework for Rooftop Unit Diagnostics</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Heating, Ventilating, Air-Conditioning and Refrigeration Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Krus</style></author><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Klaus Sedlbauer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of software tools for moisture protection of buildings in different climate zones</style></title><secondary-title><style face="normal" font="default" size="100%">6th International Conference on Cold Climate, Heating, Ventilating and Air-Conditioning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pub-location><style face="normal" font="default" size="100%">Sisimiut, Groenland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The application of software tools for moisture protection of buildings in different climatic zones is demonstrated in this paper. The basics of the programs are presented together with a typical application for a problem specific for the chosen climatic zone. A 1-D calculation has been performed for tropical climate zone with the improvement of a flat roof in Bangkok as an example. For half timbered buildings, which are common in the temperate zone with the 2-D model an infill insulation and its benefits are demonstrated. Finally the combined appliance of the whole building model and the mould risk prognosis model is shown in detail as a special case for the cold climate zone: In heated buildings of cold climate zones the internal climate with its low relative humidity in wintertime often causes discomfort and health problems for the occupants. In case of using air humidifier the risk of mould growth increases. Instead of an uncontrolled humidifying of the dry air an innovativecontrol system using a thermal bridge, which switches the humidifier off when condensation occurs is presented. To quantify the improvement in the comfort while preventing the risk of mould growth for a typical building comparative calculations of the resulting inner climates and its consequences on comfort have been performed.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Stephen E. Selkowitz</style></author><author><style face="normal" font="default" size="100%">Mehry Yazdanian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Energy Impact of Window Technologies for Commercial Buildings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2009</style></date></pub-dates></dates><custom2><style face="normal" font="default" size="100%">LBNL-6035E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Klaus Sedlbauer</style></author><author><style face="normal" font="default" size="100%">Christoph Nytsch-Geusen</style></author><author><style face="normal" font="default" size="100%">Kurt Kießl</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neue objektorientierte hygrothermische Modell-Bibliothek zur Ermittlung des hygrothermischen und hygienischen Komforts in Räumen</style></title><secondary-title><style face="normal" font="default" size="100%">Bauphysik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">271-278</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eric Shen</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simulation-based assessment of the energy savings benefits of integrated control in office buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">daylighting</style></keyword><keyword><style  face="normal" font="default" size="100%">energy conservation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy consumption</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">energy management systems</style></keyword><keyword><style  face="normal" font="default" size="100%">lighting control systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">239-251</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The purpose of this study is to use existing simulation tools to quantify the energy savings benefits of integrated control in office buildings. An EnergyPlus medium office benchmark simulation model (V1.0_3.0) developed by the Department of Energy (DOE) was used as a baseline model for this study. The baseline model was modified to examine the energy savings benefits of three possible control strategies compared to a benchmark case across 16 DOE climate zones. Two controllable subsystems were examined: (1) dimming of electric lighting, and (2) controllable window transmission. Simulation cases were run in EnergyPlus V3.0.0 for building window-to-wall ratios (WWR) of 33% and 66%. All three strategies employed electric lighting dimming resulting in lighting energy savings in building perimeter zones ranging from 64% to 84%. Integrated control of electric lighting and window transmission resulted in heating, ventilation, and air conditioning (HVAC) energy savings ranging from –1% to 40%. Control of electric lighting and window transmission with HVAC integration (seasonal schedule of window transmission control) resulted in HVAC energy savings ranging from 3% to 43%. HVAC energy savings decreased moving from warm climates to cold climates and increased when moving from humid, to dry, to marine climates.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><work-type><style face="normal" font="default" size="100%">Research Article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rüdiger Franke</style></author><author><style face="normal" font="default" size="100%">Francesco Casella</style></author><author><style face="normal" font="default" size="100%">Martin Otter</style></author><author><style face="normal" font="default" size="100%">Katrin Proelss</style></author><author><style face="normal" font="default" size="100%">Michael Sielemann</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Standardization of thermo-fluid modeling in Modelica.Fluid 1.0</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of the 7th International Modelica Conference</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">modelica</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=13</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">13</style></edition><publisher><style face="normal" font="default" size="100%">Linköping University Electronic Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Como, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">43</style></volume><isbn><style face="normal" font="default" size="100%">978-91-7393-513-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This article discusses the Modelica.Fluid library that has been included in the Modelica Standard Library 3.1. Modelica.Fluid provides interfaces and basic components for the device-oriented modeling of one dimensional thermo-fluid flow in networks containing vessels; pipes; fluid machines; valves and fittings.&lt;/p&gt;&lt;p&gt;A unique feature of Modelica.Fluid is that the component equations and the media models as well as pressure loss and heat transfer correlations are decoupled from each other. All components are implemented such that they can be used for media from the Modelica.Media library. This means that an incompressible or compressible medium; a single or a multiple substance medium with one or more phases might be used with one and the same model as long as the modeling assumptions made hold. Furthermore;&lt;/p&gt;&lt;p&gt;trace substances are supported. Modeling assumptions can be configured globally in an outer System object. This covers in particular the initialization; uni- or bi-directional flow; and dynamic or steady-state formulation of mass; energy; and momentum balance. All assumptions can be locally refined for every component.&lt;/p&gt;&lt;p&gt;While Modelica.Fluid contains a reasonable set of component models; the goal of the library is not to provide a comprehensive set of models; but rather to provide interfaces and best practices for the treatment of issues such as connector design and implementation of energy; mass and momentum balances. Applications from various domains are presented.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brian E. Coffey</style></author><author><style face="normal" font="default" size="100%">Sam Borgeson</style></author><author><style face="normal" font="default" size="100%">Stephen E. Selkowitz</style></author><author><style face="normal" font="default" size="100%">Joshua S. Apte</style></author><author><style face="normal" font="default" size="100%">Paul A. Mathew</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Very Low Energy Building Stock: Modeling the US Commercial Building Stock to Support Policy and Innovation Planning</style></title><secondary-title><style face="normal" font="default" size="100%">Building Research and Information</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">37:5</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper describes the origin, structure and continuing development of a model of time varying energy consumption in the US commercial building stock. The model is based on a flexible structure that disaggregates the stock into various categories (e.g. by building type, climate, vintage and life-cycle stage) and assigns attributes to each of these (e.g. floor area and energy use intensity by fuel type and end use), based on historical data and user-defined scenarios for future projections. In addition to supporting the interactive exploration of building stock dynamics, the model has been used to study the likely outcomes of specific policy and innovation scenarios targeting very low future energy consumption in the building stock. Model use has highlighted the scale of the challenge of meeting targets stated by various government and professional bodies, and the importance of considering both new construction and existing buildings.&lt;/p&gt;</style></abstract><section><style face="normal" font="default" size="100%">610</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Walter F. Buhl</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Stephen E. Selkowitz</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparing computer run time of building simulation programs</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computer run time</style></keyword><keyword><style  face="normal" font="default" size="100%">doe-2</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation program</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">210-213</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents an approach to comparing computer run time of building simulation programs. The computing run time of a simulation program depends on several key factors, including the calculation algorithm and modeling capabilities of the program, the run period, the simulation time step, the complexity of the energy models, the run control settings, and the software and hardware configurations of the computer that is used to make the simulation runs. To demonstrate the approach, simulation runs are performed for several representative DOE-2.1E and EnergyPlus energy models. The computer run time of these energy models are then compared and analyzed.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Dale A. Sartor</style></author><author><style face="normal" font="default" size="100%">Paul A. Mathew</style></author><author><style face="normal" font="default" size="100%">Mehry Yazdanian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparisons of HVAC Simulations between EnergyPlus and DOE-2.2 for data centers</style></title><secondary-title><style face="normal" font="default" size="100%">ASHRAE Conference</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data center</style></keyword><keyword><style  face="normal" font="default" size="100%">doe-2</style></keyword><keyword><style  face="normal" font="default" size="100%">energy performance</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">115 Part 1</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Christoph Nytsch-Geusen</style></author><author><style face="normal" font="default" size="100%">Andreas Holm</style></author><author><style face="normal" font="default" size="100%">Klaus Sedlbauer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Object-oriented hygrothermal building physics library as a tool to predict and to ensure a thermal and hygric indoor comfort in building construction by using a Predicted-Mean-Vote (PMV) control ventilation system</style></title><secondary-title><style face="normal" font="default" size="100%">8th Nordic Symposium on Building Physics in the Nordic Countries 2008</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pub-location><style face="normal" font="default" size="100%">Copenhagen, Denmark</style></pub-location><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">pp.825-832</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The indoor temperature and humidity conditions of the building envelope are important parameters for the evaluation of the thermal and hygric indoor comfort. In the research project GENSIM a new hygrothermal building library, based on the object- and equation-oriented model description language Modelica® has been developed by the Fraunhofer Institutes IBP and FIRST. This library includes many models as for instance a hygrothermal wall model, an air volume model, a zone model, a window model and an environment model. Due to the object-oriented modelling approach, some models of this library can be configured to a complex hygrothermal room model, which can predict the time dependent indoor temperature and humidity conditions in a building construction. In this paper we will introduce in a first step the object-oriented hygrothermal room model of this library. In a second step, the validation of the room model with some field experiments will be shown. In a third step we willpresent some simulation results, we obtained by coupling the room model with an implemented Predicted-Mean-Vote (PMV) control ventilation system to predict and to ensure a thermal and hygric indoor comfort in one case study. In the conclusion, the possible range of future applications of this new hygrothermal building physics library and demands for further research are indicated.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Michael A. Moshier</style></author><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using SPARK as a Solver for Modelica</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2008</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2008</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Berkeley, CA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Michael A. Moshier</style></author><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using SPARK as a solver for Modelica</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of the 3rd SimBuild Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ibpsa.us/simbuild2008/technical_sessions/SB08-DOC-TS03-1-Wetter.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Berkeley, CA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modelica is an object-oriented acausal modeling language that is well positioned to become a de-facto standard for expressing models of complex physical systems.  To simulate a model expressed in Modelica, it needs to be translated into executable code. For generating run-time efficient code, such a translation needs to employ algebraic formula manipulations. As the SPARK solver has been shown to be competitive for generating such code but currently cannot be used with the Modelica language, we report in this paper how SPARK&#039;s symbolic and numerical algorithms can be implemented in OpenModelica, an open-source implementation of a Modelica modeling and simulation environment. We also report benchmark results that show that for our air flow network simulation benchmark, the SPARK solver is competitive with Dymola, which is believed to provide the best solver for Modelica.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-634E</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arunabha Sen</style></author><author><style face="normal" font="default" size="100%">Nibedita Das</style></author><author><style face="normal" font="default" size="100%">Ling Zhou</style></author><author><style face="normal" font="default" size="100%">Bao Hong Shen</style></author><author><style face="normal" font="default" size="100%">Sudheendra Murthy</style></author><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Coverage Problem for Sensors Embedded in Temperature Sensitive Environments</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Infocom, 2007</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">5/2007</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Anchorage, AL</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paul Bourdoukan</style></author><author><style face="normal" font="default" size="100%">Etienne Wurtz</style></author><author><style face="normal" font="default" size="100%">Maurice Spérandio</style></author><author><style face="normal" font="default" size="100%">Patrice Joubert</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global Efficiency of Direct Flow Vacuum Collectors in Autonomous Solar Desiccant Cooling: Simulation and Experimental Results</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Building Simulation 2007</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2007</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Beijing, China</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Nytsch-Geusen</style></author><author><style face="normal" font="default" size="100%">Thilo Ernst</style></author><author><style face="normal" font="default" size="100%">Peter Schwarz</style></author><author><style face="normal" font="default" size="100%">Mathias Vetter</style></author><author><style face="normal" font="default" size="100%">Andreas Holm</style></author><author><style face="normal" font="default" size="100%">Juergen Leopold</style></author><author><style face="normal" font="default" size="100%">Alexander Mattes</style></author><author><style face="normal" font="default" size="100%">Andre Nordwig</style></author><author><style face="normal" font="default" size="100%">Peter Schneider</style></author><author><style face="normal" font="default" size="100%">Christoph Wittwer</style></author><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Gerhardt Schmidt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Advanced modeling and simulation techniques in MOSILAB: A system development case study</style></title><secondary-title><style face="normal" font="default" size="100%">5th International Modelica Conference</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">2006</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pages><style face="normal" font="default" size="100%">pp.63-72</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">S. Nara</style></author><author><style face="normal" font="default" size="100%">P. Vijayan</style></author><author><style face="normal" font="default" size="100%">Tang, T.</style></author><author><style face="normal" font="default" size="100%">W. Lai</style></author><author><style face="normal" font="default" size="100%">Patrick E. Phelan</style></author><author><style face="normal" font="default" size="100%">Ravi S. Prasher</style></author><author><style face="normal" font="default" size="100%">David W. Song</style></author><author><style face="normal" font="default" size="100%">J. Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Heat and Mass Transfer</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Temperature oscillation technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Thermal conductivity</style></keyword><keyword><style  face="normal" font="default" size="100%">thermal diffusivity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S001793100600144X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">2950-2956</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid’s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">17-18</style></issue><section><style face="normal" font="default" size="100%">2950</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">S. Nara</style></author><author><style face="normal" font="default" size="100%">P. Vijayan</style></author><author><style face="normal" font="default" size="100%">Tang, T.</style></author><author><style face="normal" font="default" size="100%">W. Lai</style></author><author><style face="normal" font="default" size="100%">Patrick E. Phelan</style></author><author><style face="normal" font="default" size="100%">Ravi S. Prasher</style></author><author><style face="normal" font="default" size="100%">David W. Song</style></author><author><style face="normal" font="default" size="100%">J. Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Heat and Mass Transfer</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Temperature oscillation technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Thermal conductivity</style></keyword><keyword><style  face="normal" font="default" size="100%">thermal diffusivity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S001793100600144X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">2950-2956</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid’s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">17-18</style></issue><section><style face="normal" font="default" size="100%">2950</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Douglas Kosar</style></author><author><style face="normal" font="default" size="100%">Don Shirey</style></author><author><style face="normal" font="default" size="100%">Mangesh Basarkar</style></author><author><style face="normal" font="default" size="100%">Muthasamy Swami</style></author><author><style face="normal" font="default" size="100%">Richard Raustad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dehumidification Enhancement of Direct Expansion Systems through Component Augmentation of the Cooling Coil</style></title><secondary-title><style face="normal" font="default" size="100%">Fifteenth Symposium on Improving Building Systems in Hot and Humid Climates, July 24-26, 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pub-location><style face="normal" font="default" size="100%">Orlando, FL</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Diverse air conditioning products with enhanced dehumidification features are being introduced to meet the increased moisture laden ventilation air requirements of ASHRAE Standard 62 in humid climates. In this evaluation, state point performance spreadsheet models for single path, mixed air packaged systems compare a conventional &quot;off the shelf&quot; direct expansion (DX) cooling system and its performance to systems that augment the DX coil with enhanced dehumidification components, such as heat exchangers and desiccant dehumidifiers. Using common performance metrics for comparisons at ARI rating conditions, these alternative systems define a best practice for enhanced dehumidification performance. The state point performance spreadsheet models combine available algorithms from the EnergyPlus™ simulation program for DX coils and heat exchangers with newly developed algorithms for desiccant dehumidifiers. All the models and their algorithms are applied in EnergyPlus™ for simulations of annual system cooling performance, including sensible and latent loads met, energy consumed, and humidity levels maintained, in select building types and climatic locations. Per this EnergyPlus™ analysis, these enhanced dehumidification systems present challenging decision-making tradeoffs between humidity control improvements over conventional DX systems, condensing (compressor) unit energy consumption reductions versus DX cool and reheat approaches, and fan energy use increases due to the additional component pressure drops.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Robert J. Hitchcock</style></author><author><style face="normal" font="default" size="100%">Kenneth L. Gillespie</style></author><author><style face="normal" font="default" size="100%">Martha Brook</style></author><author><style face="normal" font="default" size="100%">Christine Shockman</style></author><author><style face="normal" font="default" size="100%">Joseph J Deringer</style></author><author><style face="normal" font="default" size="100%">Kristopher L. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a Model Specification for Performance Monitoring Systems for Commercial Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">2006 ACEEE Summer Study on Energy Efficiency in Buildings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Pacific Grove, CA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Robert J. Hitchcock</style></author><author><style face="normal" font="default" size="100%">Kenneth L. Gillespie</style></author><author><style face="normal" font="default" size="100%">Martha Brook</style></author><author><style face="normal" font="default" size="100%">Christine Shockman</style></author><author><style face="normal" font="default" size="100%">Joseph J Deringer</style></author><author><style face="normal" font="default" size="100%">Kristopher L. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a Model Specification for Performance Monitoring Systems for Commercial Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">2006 ACEEE Summer Study on Energy Efficiency in Buildings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aceee.org/proceedings-paper/ss06/panel03/paper10</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Asilomar, California, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper describes the development of a model specification for performance monitoring systems for commercial buildings. The specification focuses on four key aspects of performance monitoring:  performance metrics  measurement system requirements  data acquisition and archiving  data visualization and reporting   The aim is to assist building owners in specifying the extensions to their control systems that are required to provide building operators with the information needed to operate their buildings more efficiently and to provide automated diagnostic tools with the information required to detect and diagnose faults and problems that degrade energy performance.  The paper reviews the potential benefits of performance monitoring, describes the specification guide and discusses briefly the ways in which it could be implemented. A prototype advanced visualization tool is also described, along with its application to performance monitoring. The paper concludes with a description of the ways in which the specification and the visualization tool are being disseminated and deployed.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Hanson</style></author><author><style face="normal" font="default" size="100%">Steven Carlson</style></author><author><style face="normal" font="default" size="100%">Dan Sammartano</style></author><author><style face="normal" font="default" size="100%">Thomas Taylor</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Halfway to Zero Energy in a Large Office Building</style></title><secondary-title><style face="normal" font="default" size="100%">2006 ACEEE Summer Study on Energy Efficiency in Buildings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Pacific Grove, CA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mangesh Basarkar</style></author><author><style face="normal" font="default" size="100%">Muthasamy Swami</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IFC to CONTAM Translator</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Boston, MA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kwang Ho Lee</style></author><author><style face="normal" font="default" size="100%">Richard K. Strand</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Implementation of an Earth Tube System Into EnergyPlus Program</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Cambridge, MA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Nytsch-Geusen</style></author><author><style face="normal" font="default" size="100%">Andre Nordwig</style></author><author><style face="normal" font="default" size="100%">Mathias Vetter</style></author><author><style face="normal" font="default" size="100%">Christoph Wittwer</style></author><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Peter Schneider</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MOSILAB: Ein Modelica-Simulationswerkzeug zur energetischen Gebäude- und Anlagensimulation</style></title><secondary-title><style face="normal" font="default" size="100%">16. Symposium Thermische Solarenergie</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pub-location><style face="normal" font="default" size="100%">Bad Staffelstein, Germany</style></pub-location><isbn><style face="normal" font="default" size="100%">3-934681-45-X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Vijayan</style></author><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">S. Nara</style></author><author><style face="normal" font="default" size="100%">W. Lai</style></author><author><style face="normal" font="default" size="100%">Patrick E. Phelan</style></author><author><style face="normal" font="default" size="100%">Ravi S. Prasher</style></author><author><style face="normal" font="default" size="100%">David W. Song</style></author><author><style face="normal" font="default" size="100%">J. Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of Particle Material on the Static Thermal Conductivity of Nanofluids</style></title><secondary-title><style face="normal" font="default" size="100%">Heat Transfer Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2005</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">San Francisco, CA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Nara</style></author><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">P. Vijayan</style></author><author><style face="normal" font="default" size="100%">W. Lai</style></author><author><style face="normal" font="default" size="100%">W. Rosenthal</style></author><author><style face="normal" font="default" size="100%">Patrick E. Phelan</style></author><author><style face="normal" font="default" size="100%">Ravi S. Prasher</style></author><author><style face="normal" font="default" size="100%">David W. Song</style></author><author><style face="normal" font="default" size="100%">Jinlin Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Experimental Determination of the Effect of Varying Base Fluid and Temperature on the Static Thermal Conductivity of Nanofluids</style></title><secondary-title><style face="normal" font="default" size="100%">ASME International Mechanical Engineering Congress and Exposition, November 5-11, 2005</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2005</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ASME</style></publisher><pub-location><style face="normal" font="default" size="100%">Orlando, FL</style></pub-location><isbn><style face="normal" font="default" size="100%">0-7918-4221-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The heat transfer abilities of fluids can be improved by adding small particles of sizes of the order of nanometers. Recently a lot of research has been done in evaluating the thermal conductivity of nanofluids using various nanoparticles. In our present work we address this issue by conducting a series of experiments to determine the effective thermal conductivity of alumina-nanofluids by varying the base fluid with water and antifreeze liquids like ethylene glycol and propylene glycol. Temperature oscillation method is used to find the thermal conductivity of the nanofluid. The results show the thermal conductivity enhancement of nanofluids depends on viscosity of the base fluid. Finally the results are validated with a recently proposed theoretical model.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Nytsch-Geusen</style></author><author><style face="normal" font="default" size="100%">Thilo Ernst</style></author><author><style face="normal" font="default" size="100%">Peter Schneider</style></author><author><style face="normal" font="default" size="100%">Mathias Vetter</style></author><author><style face="normal" font="default" size="100%">Andreas Holm</style></author><author><style face="normal" font="default" size="100%">Juergen Leopold</style></author><author><style face="normal" font="default" size="100%">Ullrich Doll</style></author><author><style face="normal" font="default" size="100%">Andre Nordwig</style></author><author><style face="normal" font="default" size="100%">Peter Schwarz</style></author><author><style face="normal" font="default" size="100%">Christoph Wittwer</style></author><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Gerhardt Schmidt</style></author><author><style face="normal" font="default" size="100%">Alexander Mattes</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MOSILAB: Development of a modelica based generic simulation tool supporting modal structural dynamics</style></title><secondary-title><style face="normal" font="default" size="100%">4th International Modelica Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><pub-location><style face="normal" font="default" size="100%">Hamburg, Germany</style></pub-location><pages><style face="normal" font="default" size="100%">pp.527-534</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">Saha, S.K.</style></author><author><style face="normal" font="default" size="100%">Ajay K. Yadav</style></author><author><style face="normal" font="default" size="100%">Patrick E. Phelan</style></author><author><style face="normal" font="default" size="100%">Ravi S. Prasher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Brownian Dynamics Simulation to Determine the Effective Thermal Conductivity of Nanofluids</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Applied Physics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">complex fluids</style></keyword><keyword><style  face="normal" font="default" size="100%">Disperse systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Thermal conduction in nonmetallic liquids</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2004</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">95</style></volume><pages><style face="normal" font="default" size="100%">6492–6494</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A nanofluid is a fluid containing suspended solid particles, with sizes on the order of nanometers. Normally, nanofluids have higher thermal conductivities than their base fluids. Therefore, it is of interest to predict the effective thermal conductivity of such a nanofluid under different conditions, especially since only limited experimental data are available. We have developed a technique to compute the effective thermal conductivity of a nanofluid using Brownian dynamics simulation, which has the advantage of being computationally less expensive than molecular dynamics, and have coupled that with the equilibrium Green-Kubo method. By comparing the results of our calculation with the available experimental data, we show that our technique predicts the thermal conductivity of nanofluids to a good level of accuracy.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><section><style face="normal" font="default" size="100%">6492</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yu Joe Huang</style></author><author><style face="normal" font="default" size="100%">Robin Mitchell</style></author><author><style face="normal" font="default" size="100%">Stephen E. Selkowitz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of Trade-Off Equations for EnergyStar Windows</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2004, Building Sustainability and Performance Through Simulation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2004</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Boulder, Colorado, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">LBNL-55517</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Drury B. Crawley</style></author><author><style face="normal" font="default" size="100%">Linda K. Lawrie</style></author><author><style face="normal" font="default" size="100%">Curtis O. Pedersen</style></author><author><style face="normal" font="default" size="100%">Frederick C. Winkelmann</style></author><author><style face="normal" font="default" size="100%">Michael J. Witte</style></author><author><style face="normal" font="default" size="100%">Richard K. Strand</style></author><author><style face="normal" font="default" size="100%">Richard J. Liesen</style></author><author><style face="normal" font="default" size="100%">Walter F. Buhl</style></author><author><style face="normal" font="default" size="100%">Yu Joe Huang</style></author><author><style face="normal" font="default" size="100%">Robert H. Henninger</style></author><author><style face="normal" font="default" size="100%">Jason Glazer</style></author><author><style face="normal" font="default" size="100%">Daniel E. Fisher</style></author><author><style face="normal" font="default" size="100%">Don B. Shirley</style></author><author><style face="normal" font="default" size="100%">Brent T. Griffith</style></author><author><style face="normal" font="default" size="100%">Peter G. Ellis</style></author><author><style face="normal" font="default" size="100%">Lixing Gu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EnergyPlus: An Update</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2004, Building Sustainability and Performance Through Simulation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2004</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Boulder, Colorado, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">P. Vijayan</style></author><author><style face="normal" font="default" size="100%">Tang, T.</style></author><author><style face="normal" font="default" size="100%">S. Nara</style></author><author><style face="normal" font="default" size="100%">Patrick E. Phelan</style></author><author><style face="normal" font="default" size="100%">Ravi S. Prasher</style></author><author><style face="normal" font="default" size="100%">J. Wang</style></author><author><style face="normal" font="default" size="100%">David W. Song</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of the Temperature Oscillation Technique to Calculate Thermal Conductivity of Water and Systematic Measurement of the Thermal Conductivity of Aluminum Oxide – Water Nanofluiids</style></title><secondary-title><style face="normal" font="default" size="100%">International Mechanical Engineering Congress &amp; Exposition,</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2004</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Anaheim, CA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author><author><style face="normal" font="default" size="100%">Michael A. Moshier</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graph-theoretic Methods in Simulation Using SPARK</style></title><secondary-title><style face="normal" font="default" size="100%">High Performance Computing Symposium of the Advanced Simulation Technologies Conference</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Society for Modeling Simulation International</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2004</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper deals with simulation modeling of nonlinear, deterministic, continuous systems. It describes how the Simulation Problem Analysis and Research Kernel (SPARK) uses the mathematical graph both to describe models of such systems, and to solve the embodied differential-algebraic equation systems (DAEs). Problems are described declaratively rather than algorithmically, with atomic objects representing individual equations and macro objects representing larger programming entities (submodels) in a smooth hierarchy. Internally, in a preprocessing step, graphs are used to represent the problem at the level of equations and variables rather than procedural, multi-equation blocks. Benefits obtained include models that are without predefined input and output sets, enhancing modeling flexibility and code reusability, and relieving the modeler from manual algorithm development. Moreover, graph algorithms are used for problem decomposition and reduction, greatly reducing solution time for wide classes of problems. After describing the methodology the paper presents results of benchmark tests that quantify performance advantages relative to conventional methods. In a somewhat contrived nonlinear example we show O performance as opposed</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author><author><style face="normal" font="default" size="100%">Michael A. Moshier</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Dimitri Curtil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graph-Theoretic Methods in Simulation Using SPARK</style></title><secondary-title><style face="normal" font="default" size="100%">High Performance Computing Symposium of the Advanced Simulation Technologies Conference (Society for Modeling Simulation International)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2004</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Arlington, Virginia, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richard K. Strand</style></author><author><style face="normal" font="default" size="100%">Richard J. Liesen</style></author><author><style face="normal" font="default" size="100%">Michael J. Witte</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Resources for Teaching Building Energy Simulation</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2004, Building Sustainability and Performance Through Simulation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2004</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Boulder, Colorado, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter G. Ellis</style></author><author><style face="normal" font="default" size="100%">Richard K. Strand</style></author><author><style face="normal" font="default" size="100%">Kurt T. Baumgartner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simulation of Tubular Daylighting Devices and Daylighting Shelves in EnergyPlus</style></title><secondary-title><style face="normal" font="default" size="100%">SimBuild 2004, Building Sustainability and Performance Through Simulation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2004</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Boulder, Colorado, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">Saha, S.K.</style></author><author><style face="normal" font="default" size="100%">Ajay K. Yadav</style></author><author><style face="normal" font="default" size="100%">Patrick E. Phelan</style></author><author><style face="normal" font="default" size="100%">Ravi S. Prasher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Determining the Effective Thermal Conductivity of a Nanofluid Using Brownian Dynamics Simulation</style></title><secondary-title><style face="normal" font="default" size="100%">National Heat Transfer Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2003</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Las Vegas, NV</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vladimir Bazjanac</style></author><author><style face="normal" font="default" size="100%">James Forester</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Darko Sucic</style></author><author><style face="normal" font="default" size="100%">Peng Xu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HVAC Component Data Modeling Using Industry Foundation Classes</style></title><secondary-title><style face="normal" font="default" size="100%">System Simulation in Buildings ’02</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2002</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Liège, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Industry Foundation Classes (IFC) object data model of buildings is being developed by the International Alliance for Interoperability (IAI). The aim is to support data sharing and exchange in the building and construction industry across the life-cycle of a building.&lt;/p&gt;&lt;p&gt;This paper describes a number of aspects of a major extension of the HVAC part of the IFC data model. First is the introduction of a more generic approach for handling HVAC components. This includes type information, which corresponds to catalog data, occurrence information, which defines item-specific attributes such as location and connectivity, and performance history information, which documents the actual performance of the component instance over time. Other IFC model enhancements include an extension of the connectivity model used to specify how components forming a system can be traversed and the introduction of time-based data streams.&lt;/p&gt;&lt;p&gt;This paper includes examples of models of particular types of HVAC components, such as boilers and actuators, with all attributes included in the definitions. The paper concludes by describing the on-going process of model testing, implementation and integration into the complete IFC model and how the model can be used by software developers to support interoperability between HVAC-oriented design and analysis tools.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-51365</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richard A. Buswell</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author><author><style face="normal" font="default" size="100%">Jonathan A. Wright</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-Linear Recursive Parameter Estimation Applied to Fault Detection and Diagnosis in Real Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">System Simulation in Buildings ’02</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2002</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Liège, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Priya Sreedharan</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of Chiller Models for use in Model-Based Fault Detection</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference for Enhancing Building Operations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2001</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Austin, TX</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD).  Factors that are considered in evaluating a model include accuracy, training data requirements, calibration effort, generality, and computational requirements. The objective of this study was to evaluate different modeling approaches for their applicability to model based FDD of vapor compression chillers.  Three different models were studied: the Gordon and Ng Universal Chiller model (2nd generation) and a modified version of the ASHRAE Primary Toolkit model, which are both based on first principles, and the DOE-2 chiller model, as implemented in CoolToolsTM, which is empirical. The models were compared in terms of their ability to reproduce the observed performance of an older, centrifugal chiller operating in a commercial office building and a newer centrifugal chiller in a laboratory.  All three models displayed similar levels of accuracy. Of the first principles models, the Gordon-Ng model has the advantage of being linear in the parameters, which allows more robust parameter estimation methods to be used and facilitates estimation of the uncertainty in the parameter values. The ASHRAE Toolkit Model may have advantages when refrigerant temperature measurements are also available. The DOE-2 model can be expected to have advantages when very limited data are available to calibrate the model, as long as one of the previously identified models in the CoolTools library matches the performance of the chiller in question.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-48149</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Solution Strategies for Building Energy System Simulation</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building energy systems</style></keyword><keyword><style  face="normal" font="default" size="100%">computational efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">graph theory applications</style></keyword><keyword><style  face="normal" font="default" size="100%">hvac simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">hvacsim+ models</style></keyword><keyword><style  face="normal" font="default" size="100%">spark models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2001</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">309-317</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The efficiencies of methods employed in solution of building simulation models are considered and compared by means of benchmark testing. Direct comparisons between the Simulation Problem Analysis and Research Kernel (SPARK) and the HVACSIM+ programs are presented, as are results for SPARK versus conventional and sparse matrix methods. An indirect comparison between SPARK and the IDA program is carried out by solving one of the benchmark test suite problems using the sparse methods employed in that program. The test suite consisted of two problems chosen to span the range of expected performance advantage. SPARK execution times versus problem size are compared to those obtained with conventional and sparse matrix implementations of these problems. Then, to see if the results of these limiting cases extend to actual problems in building simulation, a detailed control system for a heating, ventilating and air conditioning (HVAC) system is simulated with and without the use of SPARK cut set reduction. Execution times for the reduced and non-reduced SPARK models are compared with those for an HVACSIM+ model of the same system. Results show that the graph-theoretic techniques employed in SPARK offer significant speed advantages over the other methods for significantly reducible problems and that by using sparse methods in combination with graph-theoretic methods even problem portions with little reduction potential can be solved efficiently.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-45936</style></custom2><section><style face="normal" font="default" size="100%">309</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author><author><style face="normal" font="default" size="100%">David Claridge</style></author><author><style face="normal" font="default" size="100%">Mingsheng Liu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of Whole Building Simulation in On-Line Performance Assessment: Modeling and Implementation Issues</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation ’01</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2001</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Rio de Janeiro</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The application of model-based performance assessment at the whole building level is explored. The information requirements for a simulation to predict the actual performance of a particular real building, as opposed to estimating the impact of design options, are addressed with particular attention to common sources of input error and important deficiencies in most simulation models. The role of calibrated simulations is discussed. The communication requirements for passive monitoring and active testing are identified and the possibilities for using control system communications protocols to link on-line simulation and energy management and control systems are discussed. The potential of simulation programs to act as &quot;plug-and-play&quot; components on building control networks is discussed.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Simon J. Rees</style></author><author><style face="normal" font="default" size="100%">Jeffrey D. Spitler</style></author><author><style face="normal" font="default" size="100%">Michael J. Holmes</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of Peak Load Predictions and Treatment of Solar Gains in the Admittance and Heat Balance Load Calculation Procedures</style></title><secondary-title><style face="normal" font="default" size="100%">Building Services Engineering Research &amp; Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bse.sagepub.com/content/21/2/125</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Calculation of design cooling loads is of critical concern to designers of HVAC systems. The work reported here has been carried out under a joint ASHRAE-CIBSE research project to compare design cooling calculation methods. Peak cooling loads predicted by the ASHRAE heat balance method are compared with those predicted by a number of implementations of the admittance method using different window models. The results presented show the general trends in overprediction or underprediction of peak load. Particular attention is given to different window modelling practices. The performance of the methods is explained in terms of some of the underlying assumptions in the window models, and by reference to specific inter-model comparisons.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Simon J. Rees</style></author><author><style face="normal" font="default" size="100%">Jeffrey D. Spitler</style></author><author><style face="normal" font="default" size="100%">Morris G. Davies</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Qualitative Comparison of North American and U.K. Cooling Load Calculation Procedures</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Heating, Ventilating, Air-Conditioning and Refrigeration Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">75-99</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A qualitative comparison is presented between three current North American and U.K. design cooling load calculation methods. The methods compared are the ASHRAE Heat Balance Method, the Radiant Time Series Method and the Admittance Method, used in the U.K. The methods are compared and contrasted in terms of their overall structure. In order to generate the values of the 24 hourly cooling loads, comparison was also made in terms of the processing of the input data and the solution of the equations required. Specific comparisons are made between the approximations used by the three calculation methods to model some of the principal heat transfer mechanisms. Conclusions are drawn regarding the ability of the simplified methods to correctly predict peak-cooling loads compared to the Heat Balance Method predictions. Comment is also made on the potential for developing similar approaches to cooling load calculation in the U.K. and North America in the future.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">75</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Numerical Performance of a Graph-Theoretic HVAC Simulation Program</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation ’99</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/1999</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ibpsa.org/proceedings/BS1999/BS99_A-05.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Kyoto, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Simulation Problem Analysis and Research Kernel (SPARK) uses graph-theoretic techniques to match equations to variables and build computational graphs, yielding solution sequences indicated by needed data flow. Additionally, the problem graph is decomposed into strongly connected components, thus reducing the size of simultaneous equation sets, and small cut sets are determined, thereby reducing the number of iteration variables needed to solve each equation set. The improvement in computational efficiency produced by this graph theoretic preprocessing depends on the nature of the problem. The paper explores the improvement one might expect in practice in three ways. First, two problems chosen to span the range of performance are studied and some of the factors determining the performance are identified and discussed. The problem selected to exhibit a large improvement consists of a set of sparsely coupled non-linear equations. The problem selected to represent the other end of the performance spectrum is a set of equations obtained by discretizing Laplace&#039;s equation in two dimensions, e.g. a heat conduction problem. Execution time versus problem size is compared to that obtained with sparse matrix implementations of the same problems. Then, to see if the results of these somewhat contrived limiting cases extend to actual problems in building simulation, a detailed control system model of a six- zone VAV HVAC system is simulated with and without the use of cut set reduction. Execution times are compared between the reduced and non-reduced SPARK models, and with those from an HVACSIM+ model of the same system.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The application of Problem Reduction Techniques Based on Graph Theory to the Simulation of Non-Linear Continuous Systems</style></title><secondary-title><style face="normal" font="default" size="100%">EUROSIM’98</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><pub-location><style face="normal" font="default" size="100%">Manchester, UK</style></pub-location><pages><style face="normal" font="default" size="100%">pp. 203-207</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jeffrey D. Spitler</style></author><author><style face="normal" font="default" size="100%">Simon J. Rees</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of North American and U.K. Cooling Load Calculation Procedures - Methodology</style></title><secondary-title><style face="normal" font="default" size="100%">ASHRAE Transactions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">104</style></volume><pages><style face="normal" font="default" size="100%">47-61</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper describes the methodology used in a quanti- tative comparison between the current North American and United Kingdom cooling load calculation methods. Three calculation methods have been tested as part of a joint ASHRAE/CIBSE research project: the ASHRAE heat balance method and radiant time series method and the admittance method, used in the U.K. A companion paper (Rees et al.1998) describes the results of the study. The quantitative comparison is primarily organized as a parametric study—each building zone/weather day combination compared may be thought of as a combination of various parameters, e.g., exterior wall type, roof type, glazing area, etc. Specifically, this paper describes the overall organization of the study, the parameters and parameter levels that can be varied, and the tools developed to create input files, automate the load calculations, and extract the results. A brief descrip- tion of the cooling load calculation procedure implementa- tions is also given. The methodology presented and the tools described could also be used to make comparisons between other calculation methods.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">47</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Simon J. Rees</style></author><author><style face="normal" font="default" size="100%">Jeffrey D. Spitler</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of North American and U.K. Cooling Load Calculation Procedures - Results</style></title><secondary-title><style face="normal" font="default" size="100%">ASHRAE Transactions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">104</style></volume><pages><style face="normal" font="default" size="100%">36-46</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Calculation of design cooling loads is of critical concern to designers of HVAC systems. The work reported here has been carried out under a joint ASHRAE/CIBSE research project to compare design cooling calculation methods. Three calculation methods have been tested, the ASHRAE heat balance method and radiant time series method, and the admit- tance method, used in the U.K. The results presented in this paper show the general trends in over/underprediction of peak load in the simplified methods compared to the heat balance method. The performance of the simplified methods is explained in terms of some of the underlying assumptions in the methods and by reference to specific examples.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">36</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Edward F. Sowell</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Component-Based and Equation-Based Solvers for HVAC Simulation: a Comparison of HVACSIM+ and SPARK</style></title><secondary-title><style face="normal" font="default" size="100%">System Simulation in Buildings &#039;98</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/98</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Liège, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richard A. Buswell</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Model-Based Approach to the Commissioning of HVAC Systems</style></title><secondary-title><style face="normal" font="default" size="100%">CLIMA 2000</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/1997</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Brussles, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author><author><style face="normal" font="default" size="100%">Jonathan A. Wright</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Condition Monitoring in HVAC Subsystems using First Principles Models</style></title><secondary-title><style face="normal" font="default" size="100%">ASHRAE Transactions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">102</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">Pt. 1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Jorgensen, D.R.</style></author><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author><author><style face="normal" font="default" size="100%">Arthur L. Dexter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development and Testing of a Prototype Tool for HVAC Control System Commissioning</style></title><secondary-title><style face="normal" font="default" size="100%">ASHRAE Transactions</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">air conditioning</style></keyword><keyword><style  face="normal" font="default" size="100%">automatic</style></keyword><keyword><style  face="normal" font="default" size="100%">commissioning</style></keyword><keyword><style  face="normal" font="default" size="100%">controls</style></keyword><keyword><style  face="normal" font="default" size="100%">prototypes</style></keyword><keyword><style  face="normal" font="default" size="100%">testing</style></keyword><keyword><style  face="normal" font="default" size="100%">year 1996</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><edition><style face="normal" font="default" size="100%">1</style></edition><pub-location><style face="normal" font="default" size="100%">Atlanta, GA</style></pub-location><volume><style face="normal" font="default" size="100%">102</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Describes a set of automated tests for use in commissioning the controls associated with coils and mixing boxes in air-handling units. The test procedures were developed using a computer simulation of an office building air conditioning system and were verified by manual testing in real buildings. A prototype automated commissioning system was then evaluated in blind tests on a large air conditioning test rig. Concludes that automated commissioning has the potential to reduce the cost and increase the thoroughness of HVAC controls commissioning. A prototype commissioning tool is under development based on the described approach.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Pt. 1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Jonathan A. Wright</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Fault Detection and Diagnosis Method Based on First Principles Models and Expert Rules</style></title><secondary-title><style face="normal" font="default" size="100%">Tsinghua HVAC-95</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/1995</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Bejing, China</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mourad Benouarets</style></author><author><style face="normal" font="default" size="100%">Arthur L. Dexter</style></author><author><style face="normal" font="default" size="100%">Richard S. Fargus</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author><author><style face="normal" font="default" size="100%">Jonathan A. Wright</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Approaches to Fault Detection and Diagnosis in Air-Conditioning System</style></title><secondary-title><style face="normal" font="default" size="100%">System Simulation in Buildings &#039;94</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/1994</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Liège, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brady Schutt</style></author><author><style face="normal" font="default" size="100%">Gene Clark</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Merino, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accuracy of a Simple Method of Estimating the Minimum Temperature of a Sealed Roof Pond</style></title><secondary-title><style face="normal" font="default" size="100%">Annual Meeting of American Section of the International Solar Energy Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1982</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/1982</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Houston, TX</style></pub-location><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">709-714</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Detailed heat flux and temperature measurements have been made in two residential scale roof pond buildings in San Antonio, Texas from July to November 1981. The minimum temprature of the 4 in deep roof pond sealed in PVC bags was approximately equal to the minimum ambient dry bulb temperature. The sensitivity of this equality to changes in meteorological conditions, maximum pond temperature and thermal load is evaluated using the measurements. Verified simulations are then used to evaluate the sensitivity of this equality to changes in the thermal load, and to changes in the depth, surface emittance and surface thermal resistance of the sealed pond in various climates. For the range of roof pond design options of interest in passive cooling of buildings, the minimum pond temperature was found to be within 2 F of the minimum ambient temperature in all climates considered. The equality of these minimum temperatures is advocated as a useful rule of thumb for feasibility assessment and as part of a simplified design methodology. The simulated minimum pond temperature was found to be surprisingly insensitive to a 50% decrease in the fraction of pond area exposed to the sky.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Speltz, J.</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Thermal Benefits and Cost Effectiveness of Earth Berming</style></title><secondary-title><style face="normal" font="default" size="100%">5th National Passive Solar Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1980</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/1980</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Amherst, MA</style></pub-location><volume><style face="normal" font="default" size="100%">5.1</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A number of advantages are claimed for earth sheltered buildings; the earth provides both insulation and thermal storage and also serves to reduce infiltration and noise. This paper seeks to quantify the thermal advantages of both earth sheltering and perimeter insulation by comparing the simulated thermal performance of an earth sheltered house, a house with perimeter insulation and a house with neither. The fuel savings are then compared to the estimated construction costs to determine cost-effectiveness. The major saving from an earth sheltered building is obtained in colder climates where the effective elevation of the frost line due to the earth berms considerably reduces the cost of the foundation.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Bentley</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Ralph E. Spencer</style></author><author><style face="normal" font="default" size="100%">David Stannard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Radio Structure of a Sample of 101 Quasars from the Parkes ±4º Survey</style></title><secondary-title><style face="normal" font="default" size="100%">Monthly Notices of the Royal Astronomical Society</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Angular Distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">Astronomical Catalogs</style></keyword><keyword><style  face="normal" font="default" size="100%">Quasars</style></keyword><keyword><style  face="normal" font="default" size="100%">Radio Emission</style></keyword><keyword><style  face="normal" font="default" size="100%">Radio Sources (Astronomy)</style></keyword><keyword><style  face="normal" font="default" size="100%">Radio Spectra</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectrum Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Ultrahigh Frequencies</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1976</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/1976</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">176</style></volume><pages><style face="normal" font="default" size="100%">275-306</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">275</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Bentley</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Ralph E. Spencer</style></author><author><style face="normal" font="default" size="100%">David Stannard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High Resolution Observations of Extended Radio Sources at 1666 MHz</style></title><secondary-title><style face="normal" font="default" size="100%">Monthly Notices of the Royal Astronomical Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1975</style></year></dates><volume><style face="normal" font="default" size="100%">173</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Robin G. Conway</style></author><author><style face="normal" font="default" size="100%">David Stannard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Polarization of Radio Sources at 31 CM</style></title><secondary-title><style face="normal" font="default" size="100%">Monthly Notices of the Royal Astronomical Society</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Astronomical Catalogs</style></keyword><keyword><style  face="normal" font="default" size="100%">extragalactic radio sources</style></keyword><keyword><style  face="normal" font="default" size="100%">faraday effect</style></keyword><keyword><style  face="normal" font="default" size="100%">interferometry</style></keyword><keyword><style  face="normal" font="default" size="100%">microwave emission</style></keyword><keyword><style  face="normal" font="default" size="100%">polarized electromagnetic radiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Quasars</style></keyword><keyword><style  face="normal" font="default" size="100%">radiant flux density</style></keyword><keyword><style  face="normal" font="default" size="100%">radio astronomy</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">tables (data)</style></keyword><keyword><style  face="normal" font="default" size="100%">very high frequencies</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1974</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/1974</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">169</style></volume><pages><style face="normal" font="default" size="100%">117-131</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Measurements of the linear polarization of extragalactic radio sources have been made over a range of wavelengths in order to study both the properties of the sources themselves and the Faraday rotation along the line of sight to the observer. As part of a continuing program of such measurements the flux densities and integrated polarizations of 226 sources (including 134 quasars) were observed at 966 MHz (lambda 31 cm), to complement previous measurements at lambda 49 and lambda 74 cm (Conway et al. 1972). These results have been combined with others at shorter wavelengths in a discussion of the polarization properties of quasars (Conway et al. 1974). All the sources have angular sizes of 1 arcmin or less&lt;/p&gt;</style></abstract><section><style face="normal" font="default" size="100%">117</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ian W.A. Browne</style></author><author><style face="normal" font="default" size="100%">Michael Bentley</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Neil J. McEwan</style></author><author><style face="normal" font="default" size="100%">Ralph E. Spencer</style></author><author><style face="normal" font="default" size="100%">David Stannard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">QSOs of High Redshift?</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1974</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/1974</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">252</style></volume><pages><style face="normal" font="default" size="100%">209-210</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5480</style></issue><section><style face="normal" font="default" size="100%">209</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Robin G. Conway</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Philipp P. Kronberg</style></author><author><style face="normal" font="default" size="100%">David Stannard</style></author><author><style face="normal" font="default" size="100%">Jacques P. Vallée</style></author><author><style face="normal" font="default" size="100%">John F. C. Wardle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Radio Polarization of Quasars</style></title><secondary-title><style face="normal" font="default" size="100%">Monthly Notices of the Royal Astronomical Society</style></secondary-title><short-title><style face="normal" font="default" size="100%">Mon. Not. R. astr. Soc.</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">1974</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/1974</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">168</style></volume><pages><style face="normal" font="default" size="100%">137-162</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Observations over a wide range of wavelengths, 2.2 ≤ λ ≤ 73 cm, have been combined to define the wavelength variation of the degree of linear polarization m(λ) for 120 quasars with known redshift. For the majority, m(λ) decreases monotonically with increasing wavelength but for 35 sources the polarization curve is inverted at short wavelengths. A classification is given, based on both the polarization curve and the radio spectrum, and the results are interpreted in terms of the presence or absence of opaque components in the source. The depolarization which occurs at long wavelengths is accounted for by a combination of spectral effects and Faraday depolarization. For 46 steep-spectrum sources the depolarization curve appears to be dominated by the Faraday effect, and has been used to deduce the electron density within the radiating components. In this group of sources the correlation between depolarization and redshift noted by Kronberg et al. is confirmed and strengthened. A discussion is given of some theoretical models of radio sources in the light of the depolarization data.&lt;/p&gt;</style></abstract><section><style face="normal" font="default" size="100%">137</style></section></record></records></xml>