<?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%">Chen, Chien-fei</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">de Rubens, Gerardo Zarazua</style></author><author><style face="normal" font="default" size="100%">Yilmaz, Selin</style></author><author><style face="normal" font="default" size="100%">Bandurski, Karol</style></author><author><style face="normal" font="default" size="100%">Bélafi, Zsófia Deme</style></author><author><style face="normal" font="default" size="100%">De Simone, Marilena</style></author><author><style face="normal" font="default" size="100%">Bavaresco, Mateus Vinícius</style></author><author><style face="normal" font="default" size="100%">Wang, Yu</style></author><author><style face="normal" font="default" size="100%">Liu, Pei-ling</style></author><author><style face="normal" font="default" size="100%">Barthelmes, Verena M.</style></author><author><style face="normal" font="default" size="100%">Adams, Jacqueline</style></author><author><style face="normal" font="default" size="100%">D&#039;Oca, Simona</style></author><author><style face="normal" font="default" size="100%">Przybylski, Łukasz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Culture, conformity, and carbon? A multi-country analysis of heating and cooling practices in office buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Research &amp; Social Science</style></secondary-title><short-title><style face="normal" font="default" size="100%">Energy Research &amp; Social Science</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-03-2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">61</style></volume><pages><style face="normal" font="default" size="100%">101344</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 investigates human-building interaction in office spaces across multiple countries including Brazil, Italy, Poland, Switzerland, the United States, and Taiwan. We analyze social-psychological, contextual, and demographic factors to explain cross-country differences in adaptive thermal actions (i.e. cooling and heating behaviors) and conformity to the norms of sharing indoor environmental control features, an indicator of energy consumption. Specifically, personal adjustments such as putting on extra clothes are generally preferred over technological solutions such as adjusting thermostats in reaction to thermal discomfort. Social-psychological factors including attitudes, perceived behavioral control, injunctive norms, and perceived impact of indoor environmental quality on work productivity influence occupants’ intention to conform to the norms of sharing environmental control features. Lastly, accessibility to environmental control features, office type, gender, and age are also important factors. These findings demonstrate the roles of social-psychological and certain contextual factors in occupants’&lt;br /&gt;interactions with building design as well as their behavior of sharing environmental control features, both of which significantly influence building energy consumption, and thus, broader decarbonization.&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%">Chen, Yixing</style></author><author><style face="normal" font="default" size="100%">Luo, Xuan</style></author><author><style face="normal" font="default" size="100%">Luo, Na</style></author><author><style face="normal" font="default" size="100%">Lee, Sang Hoon</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ten questions on urban building energy modeling</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><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-01-2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">168</style></volume><pages><style face="normal" font="default" size="100%">106508</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Buildings in cities consume up to 70% of all primary energy. To achieve cities’ energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings.&lt;br /&gt;Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers.&lt;br /&gt;This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the 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%">Jared Langevin</style></author><author><style face="normal" font="default" size="100%">Chioke B. Harris</style></author><author><style face="normal" font="default" size="100%">Janet L. Reyna</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing the Potential to Reduce U.S. Building CO2 Emissions 80% by 2050</style></title><secondary-title><style face="normal" font="default" size="100%">Joule</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">decarbonization</style></keyword><keyword><style  face="normal" font="default" size="100%">electrification</style></keyword><keyword><style  face="normal" font="default" size="100%">emissions</style></keyword><keyword><style  face="normal" font="default" size="100%">energy models</style></keyword><keyword><style  face="normal" font="default" size="100%">energy policy analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">national climate goals</style></keyword><keyword><style  face="normal" font="default" size="100%">pathways building stock</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%">08/2019</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;Buildings are responsible for 36% of CO2 emissions in the United States and will thus be integral to climate change mitigation; yet, no studies have comprehensively assessed the potential long-term CO2 emissions reductions from the U.S. buildings sector against national goals in a way that can be regularly updated in the future. We use Scout, a reproducible and granular model of U.S. building energy use, to investigate the potential for the U.S. buildings sector to reduce CO2 emissions 80% by 2050, consistent with the U.S. Mid-Century Strategy. We find that a combination of aggressive efficiency measures, electrification, and high renewable energy penetration can reduce CO2 emissions by 72%–78% relative to 2005 levels, just short of the target. Results are sufficiently disaggregated by technology and end use to inform targeted building energy policy approaches and establish a foundation for continual reassessment of technology development pathways that drive significant long-term emissions reductions.&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%">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%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xuan Luo</style></author><author><style face="normal" font="default" size="100%">Barry Hooper</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of city buildings dataset for urban building energy modeling</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%">City building dataset</style></keyword><keyword><style  face="normal" font="default" size="100%">CityGML</style></keyword><keyword><style  face="normal" font="default" size="100%">Data mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Data standards</style></keyword><keyword><style  face="normal" font="default" size="100%">Urban Building Energy Modeling</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%">11/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0378778818316852https://api.elsevier.com/content/article/PII:S0378778818316852?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0378778818316852?httpAccept=text/plain</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">183</style></volume><pages><style face="normal" font="default" size="100%">252 - 265</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Urban building energy modeling (UBEM) is becoming a proven tool to support energy efficiency programs for buildings in cities. Development of a city-scale dataset of the existing building stock is a critical step of UBEM to automatically generate energy models of urban buildings and simulate their performance. This study introduces data needs, data standards, and data sources to develop city building datasets for UBEM. First, a literature review of data needs for UBEM was conducted. Then, the capabilities of the current data standards for city building datasets were reviewed. Moreover, the existing public data sources from several pioneer cites were studied to evaluate whether they are adequate to support UBEM. The results show that most cities have adequate public data to support UBEM; however, the data are represented in different formats without standardization, and there is a lack of common keys to make the data mapping easier. Finally, a case study is presented to integrate the diverse data sources from multiple city departments of San Francisco. The data mapping process is introduced and discussed. It is recommended to use the unique building identifiers as the common keys in the data sources to simplify the data mapping process. The integration methods and workflow are applied to other U.S. cities for developing the city-scale datasets of their existing building stock, including San Jose, Los Angeles, and Boston.&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%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xiaodong Xu</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</style></author><author><style face="normal" font="default" size="100%">Ziang Liu</style></author><author><style face="normal" font="default" size="100%">Ning Xu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm</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 Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Building network</style></keyword><keyword><style  face="normal" font="default" size="100%">Data-driven prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">District-scale</style></keyword><keyword><style  face="normal" font="default" size="100%">Long short-term memory networks</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%">04/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0306261919307494</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">248</style></volume><pages><style face="normal" font="default" size="100%">217 - 230</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;With the development of data-driven techniques, district-scale building energy prediction has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m2, compared to 12.05% and 0.63 kWh/m2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m2 for the support vector regression model.&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%">Sang Hoon Lee</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrating physics-based models with sensor data: An inverse modeling approach</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%">building performance simulation</style></keyword><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%">internal thermal mass</style></keyword><keyword><style  face="normal" font="default" size="100%">inverse model</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor data</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%">05/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S036013231930160X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">154</style></volume><pages><style face="normal" font="default" size="100%">23 - 31</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 building energy models (e.g., EnergyPlus) rely on some unknown input parameters (e.g., zone air infiltration) that are hard to measure, leading to uncertainty in simulation results especially for existing buildings with varying operating conditions. With the increasing deployment of smart thermostats, zone air temperature data are readily available, posing a new opportunity for building energy modeling if such data can be harnessed. This study presents a novel inverse modeling approach which inverses the zone air heat balance equation and uses the measured zone air temperature to analytically calculate the zone air infiltration rate and zone internal thermal mass (e.g., furniture, interior partitions), which are two important model parameters with great variability and difficult to measure. This paper introduces the technical concept and algorithms of the inverse models, their implementation in EnergyPlus, and verification using EnergyPlus simulated building performance data. The inverse modeling approach provides new opportunities for integrating data from massive IoT sensors and devices to enhance the accuracy of simulation results which are used to inform decision making on energy retrofits and efficiency improvements of existing 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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Nan Li</style></author><author><style face="normal" font="default" size="100%">Ryan Qi Wang</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification</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 occupancy</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy-Cyber-Physical Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">ensemble algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Wi-Fi probe technology</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%">02/2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">236</style></volume><pages><style face="normal" font="default" size="100%">55 - 69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;With rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most prominent platforms to improve building design and management. Researchers investigated the possibility of integrating energy management system with cyber-physical systems as energy-cyber-physical systems to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is one major source of uncertainties for energy management, ignoring it often results in energy wastes caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposed an occupancy linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilized ensemble classification algorithms to extract three types of occupancy information. It creates a data interface to link energy management system and cyber-physical systems and allows automated occupancy detection and interpretation through assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy type, the proposed model can potentially save about 26.4% of energy consumption from the cooling and ventilation demands.&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%">Xu, Xiaodong</style></author><author><style face="normal" font="default" size="100%">Yin, Chenhuan</style></author><author><style face="normal" font="default" size="100%">Wang, Wei</style></author><author><style face="normal" font="default" size="100%">Xu, Ning</style></author><author><style face="normal" font="default" size="100%">Hong, Tianzhen</style></author><author><style face="normal" font="default" size="100%">Li, Qi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Revealing Urban Morphology and Outdoor Comfort through Genetic Algorithm-Driven Urban Block Design in Dry and Hot Regions of China</style></title><secondary-title><style face="normal" font="default" size="100%">Sustainability</style></secondary-title><short-title><style face="normal" font="default" size="100%">Sustainability</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dry and hot areas; outdoor thermal comfort; urban morphology; urban performance simulation; genetic algorithm-driven</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-07-2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2071-1050/11/13/3683https://www.mdpi.com/2071-1050/11/13/3683/pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">3683</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In areas with a dry and hot climate, factors such as strong solar radiation, high temperature, low humidity, dazzling light, and dust storms can tremendously reduce people’s thermal comfort. Therefore, researchers are paying more attention to outdoor thermal comfort in urban environments as part of urban design. This study proposed an automatic workflow to optimize urban spatial forms with the aim of improvement of outdoor thermal comfort conditions, characterized by the universal thermal climate index (UTCI). A city with a dry and hot climate—Kashgar, China—is further selected as an actual case study of an urban block and Rhino &amp;amp; Grasshopper is the platform used to conduct simulation and optimization process with the genetic algorithm. Results showed that in summer, the proposed method can reduce the averaged UTCI from 31.17 to 27.43 °C, a decrease of about 3.74 °C, and reduce mean radiation temperature (MRT) from 43.94 to 41.29 °C, a decrease of about 2.65 °C.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">13</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%">Zhe Wang</style></author><author><style face="normal" font="default" size="100%">Thomas Parkinson</style></author><author><style face="normal" font="default" size="100%">Peixian Li</style></author><author><style face="normal" font="default" size="100%">Borong Lin</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%">The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes</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%">anomaly detection</style></keyword><keyword><style  face="normal" font="default" size="100%">ASHRAE global thermal comfort database</style></keyword><keyword><style  face="normal" font="default" size="100%">K-nearest neighbors</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate Gaussian</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupancy responsive controls</style></keyword><keyword><style  face="normal" font="default" size="100%">Subjective votes</style></keyword><keyword><style  face="normal" font="default" size="100%">thermal comfort</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%">03/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0360132319300861</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">151</style></volume><pages><style face="normal" font="default" size="100%">219 - 227</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants&#039; votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I &amp;amp; II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.&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%">Lee, Sang Hoon</style></author><author><style face="normal" font="default" size="100%">Hong, Tianzhen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Validation of an inverse model of zone air heat balance</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%">Energy simulation</style></keyword><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%">internal thermal mass</style></keyword><keyword><style  face="normal" font="default" size="100%">inverse model</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor data</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-08-2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">161</style></volume><pages><style face="normal" font="default" size="100%">106232</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 the validation method and results of an inverse model of zone air heat balance. The inverse model, implemented in EnergyPlus and published in a previous article [1], calculates highly uncertain model parameters such as internal thermal mass and infiltration airflow by inversely solving the zone air heat balance equation using the easy-to-measure zone air temperature data. The paper provides technical details of validation from the experiments using LBNL’s Facility for Low Energy eXperiment in Buildings (FLEXLAB) that measures zone air temperature under the controlled experiment of two levels of internal mass and four levels of infiltration airflow. The simulation results of the zone infiltration airflow and internal thermal mass from the inverse model agree well with the measured data from the FLEXLAB experiments. The validated inverse model in EnergyPlus can be used to enhance the energy modeling of existing buildings that enables energy performance assessments for energy efficiency improvements.&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%">Taoning Wang</style></author><author><style face="normal" font="default" size="100%">Gregory Ward</style></author><author><style face="normal" font="default" size="100%">Eleanor S. Lee</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient modeling of optically-complex, non-coplanar exterior shading: Validation of matrix algebraic methods</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%">bidirectional scattering distribution function (BSDF)</style></keyword><keyword><style  face="normal" font="default" size="100%">daylighting</style></keyword><keyword><style  face="normal" font="default" size="100%">exterior shading</style></keyword><keyword><style  face="normal" font="default" size="100%">solar heat gains</style></keyword><keyword><style  face="normal" font="default" size="100%">validation; building energy simulation tools</style></keyword><keyword><style  face="normal" font="default" size="100%">windows.</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://www.sciencedirect.com/science/article/pii/S0378778818302457?via%3Dihub</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">174</style></volume><pages><style face="normal" font="default" size="100%">464 - 483</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;It has long been established that shading windows with overhangs, fins, and other types of non-coplanar systems (NCS) is one of the most effective ways of controlling solar heat gains in buildings because they intercept solar radiation prior to entry into the building. Designers however often specify non-opaque materials (e.g., louvers, fritted glass, expanded metal mesh) for these systems in order to admit daylight, reduce lighting energy use, and improve indoor environmental quality. Most simulation tools rely on geometric calculations and radiosity methods to model the solar heat gain impacts of NCS and cannot model optically-complex materials or geometries. For daylighting analysis, optically-complex NCS can be modeled using matrix algebraic methods, although time-efficient parametric analysis has not yet been implemented. Determining the best design and/or material for static or operable NCS that minimize cooling, heating, and lighting energy use and peak demand requires an iterative process. This study describes and validates a matrix algebraic method that enables parametric energy analysis of NCS. Such capabilities would be useful not only for design but also for development of prescriptive energy-efficiency standards, rating and labeling systems for commercial products, development of design guidelines, and development of more optimal NCS technologies.&lt;/p&gt;&lt;p&gt;A facade or &quot;F&quot; matrix, which maps the transfer of flux from the NCS to the surface of the window, is introduced and its use is explained. A field study was conducted in a full-scale outdoor testbed to measure the daylight performance of an operable drop-arm awning. Simulated data were compared to measured data in order to validate the models. Results demonstrated model accuracy: simulated workplane illuminance was within 11-13%, surface luminance was within 16-18%, and the daylight glare probability was within 6-9% of measured results. Methods used to achieve accurate results are discussed. Results of the validation of daylighting performance are applicable to solar heat gain performance. Since exterior shading can also significantly reduce peak demand, these models enable stakeholders to more accurately assess HVAC and lighting impacts in support of grid management and resiliency goals.&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%">Xiaodong Xu</style></author><author><style face="normal" font="default" size="100%">Fenlan Luo</style></author><author><style face="normal" font="default" size="100%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xiuzhang Fu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance-Based Evaluation of Courtyard Design in China’s Cold-Winter Hot-Summer Climate Regions</style></title><secondary-title><style face="normal" font="default" size="100%">Sustainability</style></secondary-title><short-title><style face="normal" font="default" size="100%">Sustainability</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">aspect ratio</style></keyword><keyword><style  face="normal" font="default" size="100%">courtyard design</style></keyword><keyword><style  face="normal" font="default" size="100%">ecological buffer area</style></keyword><keyword><style  face="normal" font="default" size="100%">ecological effect</style></keyword><keyword><style  face="normal" font="default" size="100%">layout</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%">10/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mdpi.com/2071-1050/10/11/3950http://www.mdpi.com/2071-1050/10/11/3950/pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">3950</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Evaluates the performance of the traditional courtyard design of the Jiangnan Museum, located in Jiangsu Province. In the evaluation, the spatial layout of courtyards is adjusted, the aspect ratio is changed, and an ecological buffer space is created. To model and evaluate the performance of the courtyard design, this study applied the Computational fluid dynamics (CFD) software, Parabolic Hyperbolic Or Elliptic Numerical Integration Code Series (PHOENICS), for wind environment simulation, and the EnergyPlus-based software, DesignBuilder, for energy simulation. Results show that a good combination of courtyard layout and aspect ratio can improve the use of natural ventilation by increasing free cooling during hot summers and reducing cold wind in winters. The results also show that ecological buffer areas of a courtyard can reduce cooling loads in summer by approximately 19.6% and heating loads in winter by approximately 22.3%. The study provides insights into the optimal design of a courtyard to maximize its benefit in regulating the microclimate during both winter and summer.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</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%">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>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>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xuan Luo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Agent-Based Stochastic Occupancy Simulator</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;Occupancy has significant impacts on building performance. However, in current building performance simulation programs, occupancy inputs are static and lack diversity, contributing to discrepancies between the simulated and actual building performance. This paper presents an Occupancy Simulator that simulates the stochastic behavior of occupant presence and movement in buildings, capturing the spatial and temporal occupancy diversity. Each occupant and each space in the building are explicitly simulated as an agent with their profiles of stochastic behaviors. The occupancy behaviors are represented with three types of models: (1) the status transition events (e.g., first arrival in office) simulated with Reinhart’s LIGHTSWITCH-2002 model, (2) the random moving events (e.g., from one office to another) simulated with Wang’s homogeneous Markov chain model, and (3) the meeting events simulated with a new stochastic model. A hierarchical data model was developed for the Occupancy Simulator, which reduces the amount of data input by using the concepts of occupant types and space types. Finally, a case study of a small office building is presented to demonstrate the use of the Simulator to generate detailed annual sub-hourly occupant schedules for individual spaces and the whole building. The Simulator is a web application freely available to the public and capable of performing a detailed stochastic simulation of occupant presence and movement in buildings. Future work includes enhancements in the meeting event model, consideration of personal absent days, verification and validation of the simulated occupancy results, and expansion for use with residential 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%">Ying Cui</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%">Chan Xiao</style></author><author><style face="normal" font="default" size="100%">Xuan Luo</style></author><author><style face="normal" font="default" size="100%">Qi Zhang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China</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%">Actual weather data</style></keyword><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">Multiyear simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Peak load  </style></keyword><keyword><style  face="normal" font="default" size="100%">Typical year</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%">06/2017</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">195</style></volume><pages><style face="normal" font="default" size="100%">890-904</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Weather has significant impacts on the thermal environment and energy use in buildings. Thus, accurate weather data are crucial for building performance evaluations. Traditionally, typical year data inputs are used to represent long-term weather data. However, there is no guarantee that a single year represents the changing climate well. In this study, the long-term representation of a typical year was assessed by comparing it to a 55-year actual weather data set. To investigate the weather impact on building energy use, 559 simulation runs of a prototype office building were performed for 10 large cities covering all climate zones in China. The analysis results demonstrated that the weather data varied significantly from year to year. Hence, a typical year cannot reflect the variation range of weather fluctuations. Typical year simulations overestimated or underestimated the energy use and peak load in many cases. With the increase in computational power of personal computers, it is feasible and essential to adopt multiyear simulations for full assessments of long-term building performance, as this will improve decision-making by allowing for the full consideration of variations in building energy use.&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%">Na Luo</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Hui Li</style></author><author><style face="normal" font="default" size="100%">Rouxi Jia</style></author><author><style face="normal" font="default" size="100%">Wenguo Weng</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Analytics and Optimization of an Ice-Based Energy Storage System for Commercial Buildings</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Data Analytics</style></keyword><keyword><style  face="normal" font="default" size="100%">energy cost saving</style></keyword><keyword><style  face="normal" font="default" size="100%">heuristic strategy</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Thermal energy storage</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;Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice–based TES system in a shopping mall, calculating the system’s performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system’s operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3% per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.&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%">Xuan Luo</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</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%">Electric Load Shape Benchmarking for Small- and Medium-Sized Commercial Buildings</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">benchmarking</style></keyword><keyword><style  face="normal" font="default" size="100%">Building energy</style></keyword><keyword><style  face="normal" font="default" size="100%">cluster analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">load profile</style></keyword><keyword><style  face="normal" font="default" size="100%">load shape</style></keyword><keyword><style  face="normal" font="default" size="100%">representative load pattern</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;Small- and medium-sized commercial buildings owners and utility managers often look for opportunities for energy cost savings through energy efficiency and energy waste minimization. However, they currently lack easy access to low-cost tools that help interpret the massive amount of data needed to improve understanding of their energy use behaviors. Benchmarking is one of the techniques used in energy audits to identify which buildings are priorities for an energy analysis. Traditional energy performance indicators, such as the energy use intensity (annual energy per unit of floor area), consider only the total annual energy consumption, lacking consideration of the fluctuation of energy use behavior over time, which reveals the time of use information and represents distinct energy use behaviors during different time spans. To fill the gap, this study developed a general statistical method using 24-hour electric load shape benchmarking to compare a building or business/tenant space against peers. Specifically, the study developed new forms of benchmarking metrics and data analysis methods to infer the energy performance of a building based on its load shape. We first performed a data experiment with collected smart meter data using over 2,000 small- and medium-sized businesses in California. We then conducted a cluster analysis of the source data, and determined and interpreted the load shape features and parameters with peer group analysis. Finally, we implemented the load shape benchmarking feature in an open-access web-based toolkit (the Commercial Building Energy Saver) to provide straightforward and practical recommendations to users. The analysis techniques were generic and flexible for future datasets of other building types and in other utility territories.&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%">Simona D&#039;Oca</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Jared Langevin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Human Dimensions of Energy Use in Buildings: A Review</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;The &quot;human dimensions&quot; of energy use in buildings refer to the energy-related behaviors of key stakeholders that affect energy use over the building life cycle. Stakeholders include building designers, operators, managers, engineers, occupants, industry, vendors, and policymakers, who directly or indirectly influence the acts of designing, constructing, living, operating, managing, and regulating the built environments, from individual building up to the urban scale. Among factors driving high-performance buildings, human dimensions play a role that is as significant as that of technological advances. However, this factor is not well understood, and, as a result, human dimensions are often ignored or simplified by stakeholders. This paper presents a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area. The paper highlights research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity. This research focuses on identifying key needs for each stakeholder involved in a building&#039;s lifecycle and takes an interdisciplinary focus that spans the fields of architecture and engineering design, sociology, data science, energy policy, codes, and standards to provide targeted insights. Greater understanding of the human dimensions of energy use has several potential benefits including reductions in operating cost for building owners;enhanced comfort conditions and productivity for building occupants;more effective building energy management and automation systems for building operators and energy managers; and the integration of more accurate control logic into the next generation of human-in-the-loop technologies. The review concludes by summarizing recommendations for policy makers and industry stakeholders for developing codes, standards, and technologies that can leverage the human dimensions of energy use to reliably predict and achieve energy use reductions in the residential and commercial buildings sectors.&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%">Xuan Luo</style></author><author><style face="normal" font="default" size="100%">Khee Poh Lam</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</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%">Performance Evaluation of an Agent-based Occupancy Simulation Model</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">model performance evaluation</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy pattern</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupancy simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant presence and movement</style></keyword><keyword><style  face="normal" font="default" size="100%">verification</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%">04/2017</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">115</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.&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%">Yixing Chen</style></author><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%">Xuan Luo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simulation and visualization of energy-related occupant behavior 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%">Behavior Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">building performance</style></keyword><keyword><style  face="normal" font="default" size="100%">building simulation</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><keyword><style  face="normal" font="default" size="100%">visualization</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><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">785–798</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></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%">Da Yan</style></author><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%">Qi Zhang</style></author><author><style face="normal" font="default" size="100%">Jingjing An</style></author><author><style face="normal" font="default" size="100%">shan Hu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Thorough Assessment of China’s Standard for Energy Consumption of 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%">China</style></keyword><keyword><style  face="normal" font="default" size="100%">code and standard</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 Use Intensity</style></keyword><keyword><style  face="normal" font="default" size="100%">outcome-based code</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;China’s Design Standard for Energy Efficiency of Public Buildings (the Design Standard) is widely used in the design phase to regulate the energy efficiency of physical assets (envelope, lighting, HVAC) in buildings. However, the standard does not consider many important factors that influence the actual energy use in buildings, and this can lead to gaps between the design estimates and actual energy consumption. To achieve the national energy savings targets defined in the strategic 12&lt;sup&gt;th&lt;/sup&gt; Five-Year Plan, China developed the first standard for energy consumption of buildings GB/T51161-2016 (the Consumption Standard). This study provides an overview of the Consumption Standard, identifies its strengths and weaknesses, and recommends future improvements. The analysis and discussion of the constraint value and the leading value, two key indicators of the energy use intensity, provide insight into the intent and effectiveness of the Consumption Standard. The results indicated that consistency between China’s Design Standard GB 50189-2015 and the Consumption Standard GB/T51161-2016 could be achieved if the Design Standard used the actual building operations and occupant behavior in calculating the energy use in Chinese buildings. The development of an outcome-based code in the U.S. was discussed in comparison with China’s Consumption Standard, and this revealed the strengths and challenges associated with implementing a new compliance method based on actual energy use in buildings in the U.S. Overall, this study provides important insights into the latest developments of actual consumption-based building energy standards, and this information should be valuable to building designers and energy policy makers in China and the U.S.&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%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Xuan Luo</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%">An Agent-Based Occupancy Simulator for Building Performance Simulation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Traditionally, in building energy modeling (BEM) programs, occupancy inputs are deterministic and less indicative of real world scenarios, contributing to discrepancies between simulated and actual energy use in buildings. This paper presents an agent-based occupancy simulator, which models each occupant as an agent with specified movement events and statistics of space uses. To reduce the amount of data inputs, the simulator allows users to group occupants with similar behaviors as an occupant type, and spaces with similar function as a space type. It is a web-based application with friendly graphical user interface, cloud computing, and data storage. A case study is presented to demonstrate the usage of the occupancy simulator and its integration with EnergyPlus and obFMU. It first shows the required data inputs and the results from the occupancy simulator. Then, the generated occupant schedules are used in the EnergyPlus and obFMU simulation to evaluate the impacts of occupant behavior on building energy performance. The simulation results indicate that the occupancy simulator can capture the diversity of space’s occupancy behavior rather than the static weekly profiles, and can generate realistic occupancy schedules to support building performance simulation.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004492</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%">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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eleanor S. Lee</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</style></author><author><style face="normal" font="default" size="100%">Andrew McNeil</style></author><author><style face="normal" font="default" size="100%">Sabine Hoffmann</style></author><author><style face="normal" font="default" size="100%">Anothai Thanachareonkit</style></author><author><style face="normal" font="default" size="100%">Zhengrong Li</style></author><author><style face="normal" font="default" size="100%">Yong Ding</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of the Potential to Achieve Very Low Energy Use in Public Buildings in China with Advanced Window and Shading Systems</style></title><secondary-title><style face="normal" font="default" size="100%">Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building</style></keyword><keyword><style  face="normal" font="default" size="100%">China</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">shading</style></keyword><keyword><style  face="normal" font="default" size="100%">windows</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%">05/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">668-699</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;As rapid growth in the construction industry continues to occur in China, the increased demand for a higher standard living is driving significant growth in energy use and demand across the country. Building codes and standards have been implemented to head off this trend, tightening prescriptive requirements for fenestration component measures using methods similar to the US model energy code American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 90.1. The objective of this study is to (a) provide an overview of applicable code requirements and current efforts within China to enable characterization and comparison of window and shading products, and (b) quantify the load reduction and energy savings potential of several key advanced window and shading systems, given the divergent views on how space conditioning requirements will be met in the future.&lt;/p&gt;&lt;p&gt;System-level heating and cooling loads and energy use performance were evaluated for a code-compliant large office building using the EnergyPlus building energy simulation program. Commercially-available, highly-insulating, low-emittance windows were found to produce 24-66% lower perimeter zone HVAC electricity use compared to the mandated energy-efficiency standard in force (GB 50189-2005) in cold climates like Beijing. Low-e windows with operable exterior shading produced up to 30-80% reductions in perimeter zone HVAC electricity use in Beijing and 18-38% reductions in Shanghai compared to the standard. The economic context of China is unique since the cost of labor and materials for the building industry is so low. Broad deployment of these commercially available technologies with the proper supporting infrastructure for design, specification, and verification in the field would enable significant reductions in energy use and greenhouse gas emissions in the near term.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom2><style face="normal" font="default" size="100%">LBNL-187100</style></custom2><section><style face="normal" font="default" size="100%">668</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%">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%">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%">Sarah C. Taylor-Lange</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Energy retrofit analysis toolkit for commercial buildings: A review</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building energy retroﬁt</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy 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 simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Retroﬁt analysis tools</style></keyword><keyword><style  face="normal" font="default" size="100%">Web-based applications</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%">09/2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Elsevier Ltd.</style></publisher><volume><style face="normal" font="default" size="100%">89</style></volume><pages><style face="normal" font="default" size="100%">1087-1100</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Retrofit analysis toolkits can be used to optimize energy or cost savings from retrofit strategies, accelerating the adoption of ECMs (energy conservation measures) in buildings. This paper provides an up-todate review of the features and capabilities of 18 energy retrofit toolkits, including ECMs and the calculation engines. The fidelity of the calculation techniques, a driving component of retrofit toolkits, were evaluated. An evaluation of the issues that hinder effective retrofit analysis in terms of accessibility, usability, data requirement, and the application of efficiency measures, provides valuable insights into advancing the field forward. Following this review the general concepts were determined: (1) toolkits developed primarily in the private sector use empirically data-driven methods or benchmarking to provide ease of use, (2) almost all of the toolkits which used EnergyPlus or DOE-2 were freely accessible, but suffered from complexity, longer data input and simulation run time, (3) in general, there appeared to be a fine line between having too much detail resulting in a long analysis time or too little detail which sacrificed modeling fidelity. These insights provide an opportunity to enhance the design and development of existing and new retrofit toolkits in the future.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004503</style></custom2><section><style face="normal" font="default" size="100%">1087</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%">Cheng Li</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Da Yan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Insight into Actual Energy Use and Its Drivers in High-Performance Buildings</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">actual energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">building technologies</style></keyword><keyword><style  face="normal" font="default" size="100%">driving factors</style></keyword><keyword><style  face="normal" font="default" size="100%">high-performance buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">integrated design</style></keyword><keyword><style  face="normal" font="default" size="100%">performance rating</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Using portfolio analysis and individual detailed case studies, we studied the energy performance and drivers of energy use in 51 high-performance office buildings in the U.S., Europe, China, and other parts of Asia. Portfolio analyses revealed that actual site energy use intensity (EUI) of the study buildings varied by a factor of as much as 11, indicating significant variation in real energy use in HPBs worldwide. Nearly half of the buildings did not meet the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Standard 90.1-2004 energy target, raising questions about whether a building’s certification as high performing accurately indicates that a building is energy efficient and suggesting that improvement in the design and operation of HPBs is needed to realize their energy-saving potential. We studied the influence of climate, building size, and building technologies on building energy performance and found that although all are important, none are decisive factors in building energy use. EUIs were widely scattered in all climate zones. There was a trend toward low energy use in small buildings, but the correlation was not absolute; some small HPBs exhibited high energy use, and some large HPBs exhibited low energy use. We were unable to identify a set of efficient technologies that correlated directly to low EUIs. In two case studies, we investigated the influence of occupant behavior as well as operation and maintenance on energy performance and found that both play significant roles in realizing energy savings. We conclude that no single factor determines the actual energy performance of HPBs, and adding multiple efficient technologies does not necessarily improve building energy performance; therefore, an integrated design approach that takes account of climate, technology, occupant behavior, and operations and maintenance practices should be implemented to maximize energy savings in HPBs. These findings are intended to help architects, engineers, operators, and policy makers improve the design and operation of HPBs.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-180169</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%">Da Yan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Updates to the China Design Standard for Energy Efficiency  in Public Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Policy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building design</style></keyword><keyword><style  face="normal" font="default" size="100%">building energy standard</style></keyword><keyword><style  face="normal" font="default" size="100%">China</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">GB 50189</style></keyword><keyword><style  face="normal" font="default" size="100%">Public buildings</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><volume><style face="normal" font="default" size="100%">87</style></volume><pages><style face="normal" font="default" size="100%">187-198</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The China Design Standard for Energy Efficiency in public buildings (GB 50189) debuted in 2005 when China completed the 10th Five-Year Plan. GB 50189-2005 played a crucial role in regulating the energy efficiency in Chinese commercial buildings. The standard was recently updated in 2014 to increase energy savings targets by 30% compared with the 2005 standard. This paper reviews the major changes to the standard, including expansion of energy efficiency coverage and more stringent efficiency requirements. The paper also discusses the interrelationship of the design standard with China&#039;s other building energy standards. Furthermore, comparisons are made with ASHRAE Standard 90.1-2013 to provide contrasting differences in efficiency requirements. Finally recommendations are provided to guide the future standard revision, focusing on three areas: (1) increasing efficiency requirements of building envelope and HVAC systems, (2) adding a whole-building performance compliance pathway and implementing a ruleset based automatic code baseline model generation in an effort to reduce the discrepancies of baseline models created by different tools and users, and (3) adding inspection and commissioning requirements to ensure building equipment and systems are installed correctly and operate as designed.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004493</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>27</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Review of Existing Energy Retrofit Tools</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-6774E</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%">Cheng Li</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%">Revisit of Energy Use and Technologies of High Performance Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">2014 ASHRAE Annual Conference</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%">06/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.techstreet.com/ashrae/standards/se-14-c033-revisit-of-energy-use-and-technologies-of-high-performance-buildings</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ASHRAE</style></publisher><pub-location><style face="normal" font="default" size="100%">Seattle, WA</style></pub-location><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Energy consumed by buildings accounts for one third of the world’s total primary energy use. Associated with the conscious of energy savings in buildings, High Performance Buildings (HPBs) has surged across the world, with wide promotion and adoption of various performance rating and certification systems. It is valuable to look into the actual energy performance of HPBs and to understand their influencing factors.&lt;/p&gt;&lt;p&gt;To shed some light on this topic, this paper conducted a series of portfolio analysis based on a database of 51 high performance office buildings across the world. Analyses showed that the actual site Energy Use Intensity (EUI) of the 51 buildings varied by a factor of up to 11, indicating a large scale of variation of the actual energy performance of the current HPBs. Further analysis of the correlation between EUI and climate elucidated ubiquitous phenomenon of EUI scatter throughout all climate zones, implying that the weather is not a decisive factor, although important, for the actual energy consumption of an individual building. On the building size via EUI, analysis disclosed that smaller buildings have a tendency to achieving lower energy use. Even so, the correlation is not absolute since some large buildings demonstrated low energy use while some small buildings performed opposite. Concerning the technologies, statistics indicated that the application of some technologies had correlations with some specific building size and climate characteristic. However, it was still hard to pinpoint a set of technologies which was directly correlative with a group of low EUI buildings.&lt;/p&gt;&lt;p&gt;It is concluded that no a single factor essentially determines the actual energy performance of HPBs. To deliver energy-efficient buildings, an integrated design taking account of climate, technology, occupant behavior as well as operation and maintenance should be implemented.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-6667E</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>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%">Wen-Kuei Chang</style></author><author><style face="normal" font="default" size="100%">Hung-Wen Lin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather 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%">Actual meteorological year</style></keyword><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">Peak electricity demand</style></keyword><keyword><style  face="normal" font="default" size="100%">Typical meteorological year</style></keyword><keyword><style  face="normal" font="default" size="100%">weather data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2013</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Lawrence Berkeley National Laboratory</style></publisher><volume><style face="normal" font="default" size="100%">111</style></volume><pages><style face="normal" font="default" size="100%">333-350</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Buildings consume more than one third of the world’s total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980–2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: (1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; (2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; (3) the weather impact is greater for buildings in colder climates than warmer climates; (4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and (5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-6280E</style></custom2></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%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">William J. N. Turner</style></author><author><style face="normal" font="default" size="100%">Cheng Li</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Two-Day CERC-BEE Forum on Building Integrated Design and Occupant Behavior: Presentations and Summary</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates></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%">Hung-Wen Lin</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%">An In-Depth Analysis of Space Heating Energy Use in Office Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">ACEEE 2012 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%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation research</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation research group</style></keyword><keyword><style  face="normal" font="default" size="100%">space heating</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%">08/2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ACEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Asilomar, 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;Space heating represents the largest end use in the U.S. buildings and consumes more than 7 trillion Joules of site energy annually [USDOE]. Analyzing building space heating performance and identifying methods for saving energy are quite important. Hence, it is crucial to identify and evaluate key driving factors to space heating energy use to support the design and operation of low energy buildings.&lt;/p&gt;&lt;p&gt;In this study, the prototypical small and large-size office buildings of the USDOE commercial reference buildings, which comply with ASHRAE Standard 90.1-2004, are selected. Key design and operation factors were identified to evaluate their degrees of impact for space heating energy use. Simulation results demonstrate that some of the selected building design and operation parameters have more significant impacts on space heating energy use than others, on the other hand, good operation practice can save more space heating energy than raising design efficiency levels of an office building. Influence of weather data used in simulations on space heating energy is found to be significant. The simulated space heating energy use is further benchmarked against those from similar office buildings in two U.S. commercial buildings databases to better understand the discrepancies.&lt;/p&gt;&lt;p&gt;Simulated results from this study and space heating energy use collected from building databases can both vary in two potentially well overlapped wide ranges depending on details of building design and operation, not necessarily that simulation always under-predicts the reality.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-5732E</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%">Mark D. Levine</style></author><author><style face="normal" font="default" size="100%">Wei Feng</style></author><author><style face="normal" font="default" size="100%">Jing Ke</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Nan Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Retrofit Tool for Improving Energy Efficiency of Commercial Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">ACEEE 2012 Summer Study</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%">buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">China</style></keyword><keyword><style  face="normal" font="default" size="100%">commercial building</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency measures</style></keyword><keyword><style  face="normal" font="default" size="100%">retrofit tool</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation research group</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%">08/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://aceee.org/files/proceedings/2012/data/papers/0193-000098.pdf#page=1</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Asilomar, 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;Existing buildings will dominate energy use in commercial buildings in the United States for three decades or longer and even in China for the about two decades. Retrofitting these buildings to improve energy efficiency and reduce energy use is thus critical to achieving the target of reducing energy use in the buildings sector. However there are few evaluation tools that can quickly identify and evaluate energy savings and cost effectiveness of energy conservation measures (ECMs) for retrofits, especially for buildings in China. This paper discusses methods used to develop such a tool and demonstrates an application of the tool for a retrofit analysis. The tool builds on a building performance database with pre-calculated energy consumption of ECMs for selected commercial prototype buildings using the EnergyPlus program. The tool allows users to evaluate individual ECMs or a package of ECMs. It covers building envelope, lighting and daylighting, HVAC, plug loads, service hot water, and renewable energy. The prototype building can be customized to represent an actual building with some limitations. Energy consumption from utility bills can be entered into the tool to compare and calibrate the energy use of the prototype building. The tool currently can evaluate energy savings and payback of ECMs for shopping malls in China. We have used the tool to assess energy and cost savings for retrofit of the prototype shopping mall in Shanghai. Future work on the tool will simplify its use and expand it to cover other commercial building types and other countries.&amp;nbsp;&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-6553E</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%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Zhengwei Li</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%">BacNet and Analog/Digital Interfaces of the Building Controls Virtual Testbed</style></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><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper gives an overview of recent developments in the Building Controls Virtual Test Bed (BCVTB), a framework for co-simulation and hardware-in-the-loop.&lt;/p&gt;&lt;p&gt;First, a general overview of the BCVTB is presented. Second, we describe the BACnet interface, a link which has been implemented to couple BACnet devices to the BCVTB. We present a case study where the interface was used to couple a whole building simulation program to a building control system to assess in real-time the performance of a real building. Third, we present the ADInterfaceMCC, an analog/digital interface that allows a USB-based analog/digital converter to be linked to the BCVTB. In a case study, we show how the link was used to couple the analog/digital converter to a building simulation model for local loop control.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-5446E</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%">Thierry Stephane Nouidui</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Zhengwei Li</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">BACnet and Analog/Digital Interfaces of the Building Controls Virtual Test Bed</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of the 12th IBPSA Conference</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><pages><style face="normal" font="default" size="100%">p. 294-301</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">LBNL-5446E</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%">Xiufeng Pang</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%">Prevention of Compressor Short Cycling in Direct-Expansion (DX) Rooftop Units, Part 1: Theoretical Analysis and Simulation</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%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">117</style></volume><pages><style face="normal" font="default" size="100%">666-676</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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%">Xiufeng Pang</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%">Prevention of Compressor Short Cycling in Direct-Expansion (DX) Rooftop Units— Part 2: Field Investigation</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%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">117</style></volume><pages><style face="normal" font="default" size="100%">677-685</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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%">Mingsheng Liu</style></author><author><style face="normal" font="default" size="100%">Jingjuan Feng</style></author><author><style face="normal" font="default" size="100%">Zhan Wang</style></author><author><style face="normal" font="default" size="100%">Keke Zheng</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%">Impacts of Static Pressure Reset on VAV System Air Leakage, Fan Power, and Thermal Energy</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%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">116</style></volume><pages><style face="normal" font="default" size="100%">428-436</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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%">V. Kumar</style></author><author><style face="normal" font="default" size="100%">Bettina Frohnapfel</style></author><author><style face="normal" font="default" size="100%">Jovan Jovanović</style></author><author><style face="normal" font="default" size="100%">Michael Breuer</style></author><author><style face="normal" font="default" size="100%">Wangda Zuo</style></author><author><style face="normal" font="default" size="100%">Ibrahim Hadzić</style></author><author><style face="normal" font="default" size="100%">Richard Lechner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Anisotropy invariant Reynolds stress model of turbulence (AIRSM) and its application on attached and separated wall-bounded flows</style></title><secondary-title><style face="normal" font="default" size="100%">Flow, Turbulence and Combustion</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Anisotrpoy</style></keyword><keyword><style  face="normal" font="default" size="100%">Invariant map</style></keyword><keyword><style  face="normal" font="default" size="100%">Reynolds stress model</style></keyword><keyword><style  face="normal" font="default" size="100%">Reynolds-averaged Navier-Stokes</style></keyword><keyword><style  face="normal" font="default" size="100%">Separated wall-bounded flow</style></keyword><keyword><style  face="normal" font="default" size="100%">Turbulence</style></keyword><keyword><style  face="normal" font="default" size="100%">Turbulence modeling</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%">07/2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">83</style></volume><pages><style face="normal" font="default" size="100%">81-103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Numerical predictions with a differential Reynolds stress closure, which in its original formulation explicitly takes into account possible states of turbulence on the anisotropy-invariant map, are presented. Thus the influence of anisotropy of turbulence on the modeled terms in the governing equations for the Reynolds stresses is accounted for directly. The anisotropy invariant Reynolds stress model (AIRSM) is implemented and validated in different finite-volume codes. The standard wall-function approach is employed as initial step in order to predict simple and complex wall-bounded flows undergoing large separation. Despite the use of simple wall functions, the model performed satisfactory in predicting these flows. The predictions of the AIRSM were also compared with existing Reynolds stress models and it was found that the present model results in improved convergence compared with other models. Numerical issues involved in the implementation and application of the model are also addressed.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">81</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%">Lixia Wu</style></author><author><style face="normal" font="default" size="100%">Mingsheng Liu</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</style></author><author><style face="normal" font="default" size="100%">Gang Wang</style></author><author><style face="normal" font="default" size="100%">Thomas G. Lewis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CCLEP Reduces Energy Consumption by More than 50% for a Luxury Shopping Mall</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%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">115</style></volume><pages><style face="normal" font="default" size="100%">492-501</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 Continuous Commissioning Leading Project (CCLEP) process is an ongoing process to apply system optimization theory and advanced technologies to commercial retrofit projects. It was developed by Liu et al (2006) through a U.S. Department of Energy grant to the University of Nebraska and the Omaha Public Power District (OPPD) for continuous commissioning applications in commercial retrofit projects. The CCLEP process, procedures and seven case study results have already been presented (Liu et al 2006). &lt;/p&gt;&lt;p&gt; CCLEP was applied to a luxury shopping mall and office building. The case study building has ten single fan dual-duct VAV AHUs, 123 dual-duct pneumatic controller pressure independent terminal boxes, and a central heating and cooling plant. Major retrofit efforts include upgrading pneumatic to DDC controls for all AHUs, installing main hot deck dampers, replacing the boiler, installing VFD on fans and pumps, and installing Fan Airflow Stations (FAS) and Pump Waterflow Stations (PWS). This paper presents the optimal control strategies, which include main hot deck damper control, supply fan control integrated with FAS, return fan control, optimal control for terminal boxes, chilled water temperature and chilled water pump speed control, hot water temperature and hot water pump control. The measured hourly utility data after CCLEP show that annual HVAC electricity consumption is reduced by 56% and gas use is reduced by 36%. &lt;/p&gt;&lt;p&gt; This paper demonstrates the energy savings and system performance improvement through retrofits and optimal system control. This paper will present the case study building information, CCLEP major retrofits, CCLEP optimal control strategies, CCLEP results and conclusions&lt;/p&gt;</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%">Xiaobing Liu</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%">Comparison of energy efficiency between variable refrigerant flow systems and ground source heat pump systems</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%">doe-2</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">gshp</style></keyword><keyword><style  face="normal" font="default" size="100%">vrf</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%">42</style></volume><pages><style face="normal" font="default" size="100%">584-589</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;With the current movement towards net zero energy buildings, many technologies are promoted with emphasis on their superior energy efficiency. The variable refrigerant flow (VRF) and ground source heat pump (GSHP) systems are probably the most competitive technologies among these. However, there are few studies reporting the energy efficiency of VRF systems compared with GSHP systems. In this article, a preliminary comparison of energy efficiency between the air-source VRF and GSHP systems is presented. The computer simulation results show that GSHP system is more energy efficient than the air-source VRF system for conditioning a small office building in two selected US climates. In general, GSHP system is more energy efficient than the air-source VRV system, especially when the building has significant heating loads. For buildings with less heating loads, the GSHP system could still perform better than the air-source VRF system in terms of energy efficiency, but the resulting energy savings may be marginal.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young-Hum Cho</style></author><author><style face="normal" font="default" size="100%">Mingsheng Liu</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%">Improving Control and Operation of a Single Duct VAV System through CCLEP</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%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">115</style></volume><pages><style face="normal" font="default" size="100%">760-768</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;With the energy crisis of the early 1970s came the realization that buildings could be made much more efficient without sacrificing comfort. Over the last 30 years, use of variable air volume systems has become common practice. Many variable air volume (VAV) systems with pneumatic controls were installed in the 1980s and are still in use. However, these systems often have outdated control strategies and deficient mechanical systems are deficient, which may cause occupant discomfort and excess energy consumption.&lt;/p&gt;&lt;p&gt;An ASHRAE committee proposed building commissioning in 1988 to ensure that system performance met design specifications. Continuous Commissioning (CC[R]) technology was developed and implemented in 1992. CC is an ongoing process to resolve operating problems, improve comfort, optimize energy use and identify retrofits for existing commercial and institutional buildings and central plant facilities [1-5]. Since 1999, the Energy Systems Laboratory (ESL) at the University of Nebraska has conducted extensive research to implement optimal system control during the design phase and finalize the optimal setpoints after system installation. ESL researchers have developed and implemented the Continuous Commissioning Leading Energy Project (CCLEP) process with federal and industry support. The CCLEP process has two stages: the contracting stage and the implementation stage. During the contracting stage, a comprehensive technical evaluation is performed. The CCLEP implementation stage involves planning, retrofit and trouble shooting, and optimization and follow-up. The CCLEP process, procedures and seven case study results are presented in [6].&lt;/p&gt;&lt;p&gt;This paper presents information on the case study facility, existing and improved control sequences, and building performance improvement and energy consumption measures before and after CCLEP implementation&lt;/p&gt;</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>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tengfang T. Xu</style></author><author><style face="normal" font="default" size="100%">Chuang Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Mark D. Levine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Technical Assistance to Beichuan Reconstruction: Creating and Designing Low- to Zero-carbon Communities in New Beichuan</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%">2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.escholarship.org/uc/item/0vv4m1gb</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">LBNL</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">LBNL-2819E</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%">Danielle Monfet</style></author><author><style face="normal" font="default" size="100%">Radu Zmeureanu</style></author><author><style face="normal" font="default" size="100%">Roland Charneux</style></author><author><style face="normal" font="default" size="100%">Nicolas Lemire</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer Model of a University Building Using the EnergyPlus Program</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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jingjuan Feng</style></author><author><style face="normal" font="default" size="100%">Mingsheng Liu</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%">Economizer Control Using Mixed Air Enthalpy</style></title><secondary-title><style face="normal" font="default" size="100%">the 7th International Conference of Enhanced Building Operations</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">7th</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007</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>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred S. Bauman</style></author><author><style face="normal" font="default" size="100%">Thomas L. Webster</style></author><author><style face="normal" font="default" size="100%">Hui Jin</style></author><author><style face="normal" font="default" size="100%">Wolfgang Lukaschek</style></author><author><style face="normal" font="default" size="100%">Corinne Benedek</style></author><author><style face="normal" font="default" size="100%">Edward A. Arens</style></author><author><style face="normal" font="default" size="100%">Paul F. Linden</style></author><author><style face="normal" font="default" size="100%">Anna Lui</style></author><author><style face="normal" font="default" size="100%">Walter F. Buhl</style></author><author><style face="normal" font="default" size="100%">Darryl J. 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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. 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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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jon W. Hand</style></author><author><style face="normal" font="default" size="100%">Drury B. Crawley</style></author><author><style face="normal" font="default" size="100%">Michael Donn</style></author><author><style face="normal" font="default" size="100%">Linda K. Lawrie</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving the Data Available to Simulation Programs</style></title><secondary-title><style face="normal" font="default" size="100%">IBPSA Building Simulation 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%">08/2005</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Montreal, Canada</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;Building performance simulation tools have significantly improved in quality and depth of analysis capability over the past thirty-five years. Yet despite these increased capabilities, simulation programs still depend on user entry for significant data about building components, loads, and other typically scheduled inputs. This often forces users to estimate values or find previously compiled sets of data for these inputs. Often there is little information about how the data were derived, what purposes it is fit for, which standards apply, uncertainty associated with each data field as well as a general description of the data.&lt;/p&gt;&lt;p&gt;A similar problem bedeviled access to weather data and Crawley, Hand, and Lawrie (1999) described a generalized weather data format developed for use with two energy simulation programs which has subsequently lead to a repository which is accessed by thousands of practitioners each year.&lt;/p&gt;&lt;p&gt;This paper describes a generalized format and data documentation for user input—whether it is building envelope components, scheduled loads, or environmental emissions—the widgets upon which all models are dependant. We present several examples of the new input data format including building envelope component, a scheduled occupant load, and environmental emissions.&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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zheng, B</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</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%">Using a Fan Air Flow Station to Control Building Static Pressure in a VAV System</style></title><secondary-title><style face="normal" font="default" size="100%">the 2005 International Solar Energy 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%">2005</style></date></pub-dates></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></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%">Guilherme Carrilho da Graça</style></author><author><style face="normal" font="default" size="100%">Paul F. Linden</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%">Design and Testing of a Control Strategy for a Large Naturally Ventilated Office Building</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%">2004</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">211-221</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 design for the new Federal Building for San Francisco includes an office tower that is to be naturally ventilated. Each floor is designed to be cross-ventilated, through upper windows that are controlled by the building management system. Users have control over lower level windows, which can be as much as 50% of the total openable area. There are significant differences in the performance and the control of the windward and leeward sides of the building, and separate monitoring and control strategies are determined for each side. The performance and control of the building has been designed and tested using a modified version of EnergyPlus. Results from studies with EnergyPlus and computational fluid dynamics are used in designing the control strategy. Wind-driven cross-ventilation produces a main jet through the upper openings of the building, across the ceiling from the windward to the leeward side. Below this jet, the occupied regions are subject to a recirculating airflow. Results show that temperatures within the building are predicted to be satisfactory, provided a suitable control strategy is implemented that uses night cooling in periods of hot weather. The control strategy has 10 window opening modes. EnergyPlus was extended to simulate the effects of these modes, and to assess the effects of different forms of user behaviour. The results show how user behaviour can significantly influence the building performance.&lt;/p&gt;&lt;p&gt;(Note: PDF contains both LBNL-56010 &amp;amp; LBNL-56010 Conf.)&lt;/p&gt;</style></abstract><call-num><style face="normal" font="default" size="100%">LBNL-56010</style></call-num><custom1><style face="normal" font="default" size="100%">&lt;p&gt;Commercial Building Systems Group&lt;/p&gt;</style></custom1><custom2><style face="normal" font="default" size="100%">LBNL-56010</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%">Guilherme Carrilho da Graça</style></author><author><style face="normal" font="default" size="100%">Paul F. Linden</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%">Design and Testing of a Control Strategy for a Large Naturally Ventilated Office Building</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%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bse.sagepub.com/content/25/3/223</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">223-239</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The design for the new Federal Building for San Francisco includes an office tower that is to be naturally ventilated. Each floor is designed to be cross-ventilated, through upper windows that are controlled by the building management system. Users have control over lower level windows, which can be as much as 50% of the total openable area. There are significant differences in the performance and the control of the windward and leeward sides of the building, and separate monitoring and control strategies are determined for each side. The performance and control of the building has been designed and tested using a modified version of EnergyPlus. Results from studies with EnergyPlus and computational fluid dynamics are used in designing the control strategy. Wind-driven cross-ventilation produces a main jet through the upper openings of the building, across the ceiling from the windward to the leeward side. Below this jet, the occupied regions are subject to a recirculating airflow. Results show that temperatures within the building are predicted to be satisfactory, provided a suitable control strategy is implemented that uses night cooling in periods of hot weather. The control strategy has 10 window opening modes. EnergyPlus was extended to simulate the effects of these modes, and to assess the effects of different forms of user behaviour. The results show how user behaviour can significantly influence the building performance.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom2><style face="normal" font="default" size="100%">LBNL-56010</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>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paul F. Linden</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Flow in an Underfloor Plenum</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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Angui Li</style></author><author><style face="normal" font="default" size="100%">Phillip Jones</style></author><author><style face="normal" font="default" size="100%">Pingge Zhao</style></author><author><style face="normal" font="default" size="100%">Liping Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Heat transfer and natural ventilation from single-sided heated solar chimney for buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Asian Architecture and Building Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><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>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rahul Chillar</style></author><author><style face="normal" font="default" size="100%">Richard J. Liesen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improvement of the ASHRAE Secondary HVAC Toolkit Simple Cooling Coil Model for 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%">Nicholas Long</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Near Real-Time Weather Data Archive</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%">Liping Wang</style></author><author><style face="normal" font="default" size="100%">Angui Li</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A numerical study of vertical solar chimney for Enhancing stack ventilation in buildings</style></title><secondary-title><style face="normal" font="default" size="100%">The 21st International Conference on Passive and Low Energy Architecture, The Netherlands</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%">09/2004</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%">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>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%">Paul F. Linden</style></author><author><style face="normal" font="default" size="100%">Guilherme Carrilho da Graça</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of Simulation in the Design of a Large Naturally Ventilated Office Building</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%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bse.sagepub.com/content/25/3/211</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">211-221</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The design for the new Federal Building for San Francisco includes an office tower that is to be naturally ventilated. The EnergyPlus thermal simulation program was used to evaluate different ventilation strategies for space cooling and rationalize the design of the façade. The strategies include ventilation driven by different combinations of wind, internal stack and external stack. The simulation results indicate that wind-driven ventilation can maintain adequate comfort even during hot periods. Computational fluid dynamics was used to study the airflow and temperature distribution in the occupied spaces arising from different combinations of window openings and outside conditions and thereby inform both the design of the windows and the control strategy.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom2><style face="normal" font="default" size="100%">LBNL-56011</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 J. Liesen</style></author><author><style face="normal" font="default" size="100%">Rahul Chillar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variable Heat Recovery in Double Bundle Electric Chillers</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%">Guilherme Carrilho da Graça</style></author><author><style face="normal" font="default" size="100%">Paul F. Linden</style></author><author><style face="normal" font="default" size="100%">Erin McConahey</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%">Design and Testing of a Control Strategy for a Large Naturally Ventilated Office Building</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation ’03</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%">08/2003</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Eindhoven, Netherlands</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">LBNL-56010</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%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Guilherme Carrilho da Graça</style></author><author><style face="normal" font="default" size="100%">Paul F. Linden</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of Simulation in the Design of a Large Naturally Ventilated Commercial Office Building</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation ’03</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%">08/2003</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.inive.org/members_area/medias/pdf/Inive/IBPSA/UFSC912.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Eindhoven, Netherlands</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">LBNL-56011</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%">Michael Wetter</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Roberto Lamberts</style></author><author><style face="normal" font="default" size="100%">Cezar O. R. Negrão</style></author><author><style face="normal" font="default" size="100%">Jan Hensen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">GenOpt - A Generic Optimization Program</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of the 7th IBPSA Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ibpsa.org/proceedings/BS2001/BS01_0601_608.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Rio de Janeiro</style></pub-location><volume><style face="normal" font="default" size="100%">I</style></volume><pages><style face="normal" font="default" size="100%">601-608</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 potential offered by computer simulation is often not realized: Due to the interaction of system variables, simulation users rarely know how to choose input parameter settings that lead to optimal performance of a given system. Thus, a program called GenOpt® that automatically determines optimal parameter settings has been developed.&lt;/p&gt;&lt;p&gt;GenOpt is a generic optimization program. It minimizes an objective function with respect to multiple parameters. The objective function is evaluated by a simulation program that is iteratively called by GenOpt. In thermal building simulation — which is the main target of GenOpt — the simulation program usually has text-based I/O. The paper shows how GenOpt&#039;s simulation program interface allows the coupling of any simulation program with text based I/O by simply editing a configuration file, avoiding code modification of the simulation program. By using object-oriented programming, a high-level interface for adding minimization algorithms to GenOpt&#039;s library has been developed. We show how the algorithm interface separates the minimization algorithms and GenOpt&#039;s kernel, which allows implementing additional algorithms without being familiar with the kernel or having to recompile it. The algorithms can access utility classes that are commonly used for minimization, such as optimality check, line-search, etc.&lt;/p&gt;&lt;p&gt;GenOpt has successfully solved various optimization problems in thermal building simulation. We show an example of minimizing source energy consumption of an office building using EnergyPlus, and of minimizing auxiliary electric energy of a solar domestic hot water system using TRNSYS. For both examples, the time required to set up the optimization was less than one hour, and the energy savings are about 15%, together with better daylighting usage or lower investment costs, respectively.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-48371</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%">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>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tuomas Laine</style></author><author><style face="normal" font="default" size="100%">Risto Kosonen</style></author><author><style face="normal" font="default" size="100%">Kim Hagström</style></author><author><style face="normal" font="default" size="100%">Panu Mustakallio</style></author><author><style face="normal" font="default" size="100%">De-Wei Yin</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Qingyan Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Better IAQ Through Integrating Design Tools For The HVAC Industry</style></title><secondary-title><style face="normal" font="default" size="100%">Healthy Buildings 2000</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2000</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Espoo, Finland</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;There is currently no effective combination of interoperable design tools to cover all critical aspects of the HVAC design process. Existing design tools are separately available, but require expertise and operating time that is beyond the scope of a normal design project. For example, energy analysis and computational fluid dynamics (CFD) tools are not used in practical design, leading to poor indoor air quality and/or unnecessary energy consumption in buildings.&lt;/p&gt;&lt;p&gt;A prototype integrated software tool for demonstration, process mapping and proof-of-concept purposes was developed under a new international, Finland/USA jointly funded development project BildIT. Individual design tools were simplified and adapted to specific applications and also integrated so that they can be used in a timely and effective manner by the designer. The core of the prototype linked together an architectural CAD system, a 3D space model, a CFD program and a building energy simulation program and it utilises real product data from manufacturer&#039;s software. Also the complex building design, construction, maintenance and retrofit processes were mapped to get a template for the structure of the integrated design tool.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-48456</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%">Keck-Voon Ling</style></author><author><style face="normal" font="default" size="100%">Arthur L. Dexter</style></author><author><style face="normal" font="default" size="100%">Geng, G.</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%">Self-tuning Control with Fuzzy Rule-Based Supervision for HVAC Applications</style></title><secondary-title><style face="normal" font="default" size="100%">ITAC 91</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">International Symposium on Intelligent Tuning and Adapative Control</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><pub-location><style face="normal" font="default" size="100%">Singapore</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%">Arthur L. Dexter</style></author><author><style face="normal" font="default" size="100%">Jorgensen, D.R.</style></author><author><style face="normal" font="default" size="100%">Keck-Voon Ling</style></author><author><style face="normal" font="default" size="100%">Geng, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of a Building Emulator to Develop Techniques for Improved Commissioning and Control of HVAC Systems</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%">computer programs</style></keyword><keyword><style  face="normal" font="default" size="100%">controls</style></keyword><keyword><style  face="normal" font="default" size="100%">energy management</style></keyword><keyword><style  face="normal" font="default" size="100%">heating</style></keyword><keyword><style  face="normal" font="default" size="100%">ventilation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><volume><style face="normal" font="default" size="100%">97</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><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%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Paul J. Littlefair</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Daylight in Dynamic Thermal Modelling Programs: a Case Study</style></title><secondary-title><style face="normal" font="default" size="100%">Building Services Engineering Research &amp; Technology</style></secondary-title><short-title><style face="normal" font="default" size="100%">Building Serv Eng Res Technol</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">1988</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/1988</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">183-188</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Heating, cooling and lighting energy consumptions in buildings are inter-related, and a model which treats thermal performance and lighting simultaneously is required in order to evaluate the full benefits of daylighting in buildings. A lighting facility has been included in a dynamic building simulation program (SERI-RES) used in the Department of Energy&#039;s passive solar programme. Interior daylight illuminance is calculated using an extension of the daylight factor method. The lighting usage of various lighting systems is predicted from the daylight illuminance, and the thermal consequences of that lighting use included in the thermal simulation of the building. The applicability of the method described in this paper is not limited to SERI-RES. The method could be incorporated in any building energy analysis program intended for the UK or similar climates.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><section><style face="normal" font="default" size="100%">183</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%">Gene Clark</style></author><author><style face="normal" font="default" size="100%">Fred M. Loxsom</style></author><author><style face="normal" font="default" size="100%">Earl S. Doderer</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%">Performance of Roofpond Cooled Residences in U.S. Climate</style></title><secondary-title><style face="normal" font="default" size="100%">Passive Solar Journal</style></secondary-title><short-title><style face="normal" font="default" size="100%">Passive Sol. J.</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/1987</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">265-292</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 thermal advantages of a roofpond as an element of a residential cooling system are described. The authors conducted heat transfer experiments at two roofpond residences (RPRs) at Trinity University; the authors used data from these experiments to validate RPR simulations. Results of measurements of vertical and horizontal temperature differences within roofponds are discussed. Horizontal heat transfer within one water bag was effective. Thermal resistance at the outer surface of a water bag with a deflated glazing can be significant. Simulation shows that an RPR can provide comfort without supplemental sensible cooling during almost all hours of a typical summer in any U.S climate. Ceiling fans are important in most climates. In the most demanding climates, the residence and the pond insulating panels must have high R-value. A map is included that provides RPR design guidance. The simulations indicate that dehumidification will be required to control mold, mildew, and ceiling condensation in an RPR in most climates; energy and power displacement by an RPR is sensitive to the humidity control required and the efficiency of the dehumidifier used.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">265</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%">John G.F. Littler</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%">Development of SERI-RES within the UK Passive Solar Programme</style></title><secondary-title><style face="normal" font="default" size="100%">10th National Passive Solar Conference</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">ASES</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1986</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/1986</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Boulder, CO</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%">Gene Clark</style></author><author><style face="normal" font="default" size="100%">Fred M. Loxsom</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Earl S. Doderer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Results of Validated Simulations of Roof Pond Residences</style></title><secondary-title><style face="normal" font="default" size="100%">8th National Passive Solar Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1983</style></year></dates><pub-location><style face="normal" font="default" size="100%">Santa Fe, NM</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%">Fred M. Loxsom</style></author><author><style face="normal" font="default" size="100%">Earl S. Doderer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dehumidification and Passive Cooling for Retrofit and Conventional Construction</style></title><secondary-title><style face="normal" font="default" size="100%">7th National Passive Solar Conference</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%">Knoxville, TN</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%">Fred M. Loxsom</style></author><author><style face="normal" font="default" size="100%">Gene Clark</style></author><author><style face="normal" font="default" size="100%">Merino, M.</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%">Measurement of Components of Heat Transfer in Passive Cooling Systems</style></title><secondary-title><style face="normal" font="default" size="100%">1st International Passive &amp; Hybrid Cooling Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1981</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/1981</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Miami, FL</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>