<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luo, Na</style></author><author><style face="normal" font="default" size="100%">Weng, Wenguo</style></author><author><style face="normal" font="default" size="100%">Xu, Xiaoyu</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Fu, Ming</style></author><author><style face="normal" font="default" size="100%">Sun, Kaiyu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California</style></title><secondary-title><style face="normal" font="default" size="100%">Science of The Total Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Science of The Total Environment</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational fluid dynamics siumlation</style></keyword><keyword><style  face="normal" font="default" size="100%">human exposure risk</style></keyword><keyword><style  face="normal" font="default" size="100%">indoor air quality</style></keyword><keyword><style  face="normal" font="default" size="100%">NAPA wildfire</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">respiratory injury</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-10-2019</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">686</style></volume><pages><style face="normal" font="default" size="100%">1251 - 1261</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires—by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviours by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Felix Bunning</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Marcus Fuchs</style></author><author><style face="normal" font="default" size="100%">Dirk Muller</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bidirectional low temperature district energy systems with agent-based control: Performance comparison and operation optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">209</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">2001090</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%">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%">Brahm van der Heijde</style></author><author><style face="normal" font="default" size="100%">Marcus Fuchs</style></author><author><style face="normal" font="default" size="100%">Carles Ribas Tugores</style></author><author><style face="normal" font="default" size="100%">Gerald Schweiger</style></author><author><style face="normal" font="default" size="100%">Kevin Sartor</style></author><author><style face="normal" font="default" size="100%">Daniele Basciotti</style></author><author><style face="normal" font="default" size="100%">Dirk Muller</style></author><author><style face="normal" font="default" size="100%">Christoph Nytsch-Geusen</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Lieve Helsen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Conversion and Management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">151</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">2001049</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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%">Bing Dong</style></author><author><style face="normal" font="default" size="100%">Ardeshir Mahdavi</style></author><author><style face="normal" font="default" size="100%">Simona D&#039;Oca</style></author><author><style face="normal" font="default" size="100%">Isabella Gaetani</style></author><author><style face="normal" font="default" size="100%">Xiaohang Feng</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Building</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building performance</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">IEA EBC Annex 66</style></keyword><keyword><style  face="normal" font="default" size="100%">Interdisciplinary approach</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">156</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;More than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupant behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">William J. Fisk</style></author><author><style face="normal" font="default" size="100%">Norman Bourassa</style></author><author><style face="normal" font="default" size="100%">Wanyu R. Chan</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">H.Y. Iris Cheung</style></author><author><style face="normal" font="default" size="100%">Toshifumi Hotchi</style></author><author><style face="normal" font="default" size="100%">Margarita Kloss</style></author><author><style face="normal" font="default" size="100%">Sang Hoon Lee</style></author><author><style face="normal" font="default" size="100%">Phillip N. Price</style></author><author><style face="normal" font="default" size="100%">Oren Schetrit</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Sarah C. Taylor-Lange</style></author><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Small and Medium Building Efficiency Toolkit and Community Demonstration Program</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CBES</style></keyword><keyword><style  face="normal" font="default" size="100%">commercial buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energy savings</style></keyword><keyword><style  face="normal" font="default" size="100%">indoor air quality</style></keyword><keyword><style  face="normal" font="default" size="100%">indoor environmental quality</style></keyword><keyword><style  face="normal" font="default" size="100%">outdoor air measurement technology</style></keyword><keyword><style  face="normal" font="default" size="100%">outdoor airflow intake rate</style></keyword><keyword><style  face="normal" font="default" size="100%">retrofit</style></keyword><keyword><style  face="normal" font="default" size="100%">ventilation rate</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2017</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Small commercial buildings in the United States consume 47 percent of all primary energy consumed in the building sector. Retrofitting small and medium commercial buildings may pose a steep challenge for owners, as many lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. To address this problem, this project developed the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit that calculates the energy use of a building, identifies and evaluates retrofit measures based on energy savings, energy cost savings, and payback. The CBES Toolkit includes a web app for end users and the CBES Application Programming Interface for integrating CBES with other energy software tools. The toolkit provides a rich feature set, including the following:&lt;/p&gt;&lt;ol&gt;&lt;li&gt;Energy Benchmarking providing an Energy Star score&lt;/li&gt;&lt;li&gt;Load Shape Analysis to identify potential building operation improvements&lt;/li&gt;&lt;li&gt;Preliminary Retrofit Analysis which uses a custom developed pre-simulated database&lt;/li&gt;&lt;li&gt;Detailed Retrofit Analysis which utilizes real time EnergyPlus simulations&lt;/li&gt;&lt;/ol&gt;&lt;p&gt;In a parallel effort the project team developed technologies to measure outdoor airflow rate; commercialization and use would avoid both excess energy use from over ventilation and poor indoor air quality resulting from under ventilation.&lt;/p&gt;&lt;p&gt;If CBES is adopted by California’s statewide small office and retail buildings, by 2030 the state can anticipate 1,587 gigawatt hours of electricity savings, 356 megawatts of non-coincident peak demand savings, 30.2 megatherms of natural gas savings, $227 million of energy-related cost savings, and reduction of emissions by 757,866 metric tons of carbon dioxide equivalent. In addition, consultant costs will be reduced in the retrofit analysis process.&lt;/p&gt;&lt;p&gt;CBES contributes to the energy savings retrofit field by enabling a straightforward and uncomplicated decision-making process for small and medium business owners and leveraging different levels of assessment to match user background, preference, and data availability.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-2001054</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lei Zhang</style></author><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author><author><style face="normal" font="default" size="100%">Yu Zhang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Qinglin Meng</style></author><author><style face="normal" font="default" size="100%">Yanshan Feng</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Impact of Evaporation Process on Thermal Performance of Roofs - Model Development and Numerical Analysis</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%">Evaporative Cooling</style></keyword><keyword><style  face="normal" font="default" size="100%">model development</style></keyword><keyword><style  face="normal" font="default" size="100%">Net zero energy building</style></keyword><keyword><style  face="normal" font="default" size="100%">Numerical analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Passive techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Porous building material</style></keyword><keyword><style  face="normal" font="default" size="100%">Roof thermal performance</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></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Wetter</style></author><author><style face="normal" font="default" size="100%">Marcus Fuchs</style></author><author><style face="normal" font="default" size="100%">Thierry Stephane Nouidui</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design choices for thermofluid flow components and systems that are exported as Functional Mockup Units</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;This paper discusses design decisions for exporting Modelica thermofluid flow components as Functional Mockup Units. The purpose is to provide guidelines that will allow building energy simulation programs and HVAC equipment manufacturers to effectively use FMUs for modeling of HVAC components and systems. We provide an analysis for direct input-output dependencies of such components and discuss how these dependencies can lead to algebraic loops that are formed when connecting thermofluid flow components. Based on this analysis, we provide recommendations that increase the computing efficiency of such components and systems that are formed by connecting multiple components. We explain what code optimizations are lost when providing thermofluid flow components as FMUs rather than Modelica code. We present an implementation of a package for FMU export of such components, explain the rationale for selecting the connector variables of the FMUs and finally provide computing benchmarks for different design choices. It turns out that selecting temperature rather than specific enthalpy as input and output signals does not lead to a measurable increase in computing time, but selecting nine small FMUs rather than a large FMU increases computing time by 70%&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1002826</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%">William O&#039;Brien</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xiaohang Feng</style></author><author><style face="normal" font="default" size="100%">H. Burak Gunay</style></author><author><style face="normal" font="default" size="100%">Farhang Tahmasebi</style></author><author><style face="normal" font="default" size="100%">Ardeshir Mahdavi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Occupant Behavior Modeling for Building  Performance Simulation: Current State and Future Challenges</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</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%">11/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">107</style></volume><pages><style face="normal" font="default" size="100%">264-278</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004504</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%">Xiaohang Feng</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simulation of Occupancy in 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%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">co-simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">software module</style></keyword><keyword><style  face="normal" font="default" size="100%">stochastic modeling</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%">01/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">87</style></volume><pages><style face="normal" font="default" size="100%">348-359</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2–4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-180424</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%">Le Yang</style></author><author><style face="normal" font="default" size="100%">David Hill</style></author><author><style face="normal" font="default" size="100%">Wei Feng</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data and Analytics to Inform Energy Retrofit of High Performance Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Analytics</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 use</style></keyword><keyword><style  face="normal" font="default" size="100%">High performance buildings</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><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">126</style></volume><pages><style face="normal" font="default" size="100%">90-106</style></pages><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 in buildings with energy efficient technologies is feasible and also driven by energy policies such as energy benchmarking, disclosure, rating, and labeling in both the developed and developing countries. Current energy retrofits focus on the existing building stocks, especially older buildings, but the growing number of new high performance buildings built around the world raises a question that how these buildings perform and whether there are retrofit opportunities to further reduce their energy use. This is a new and unique problem for the building industry. Traditional energy audit or analysis methods are inadequate to look deep into the energy use of the high performance buildings. This study aims to tackle this problem with a new holistic approach powered by building performance data and analytics. First, three types of measured data are introduced, including the time series energy use, building systems operating conditions, and indoor and outdoor environmental parameters. An energy data model based on the ISO Standard 12655 is used to represent the energy use in buildings in a three-level hierarchy. Secondly, a suite of analytics were proposed to analyze energy use and to identify retrofit measures for high performance buildings. The data-driven analytics are based on monitored data at short time intervals, and cover three levels of analysis – energy profiling, benchmarking and diagnostics. Thirdly, the analytics were applied to a high performance building in California to analyze its energy use and identify retrofit opportunities, including: (1) analyzing patterns of major energy end-use categories at various time scales, (2) benchmarking the whole building total energy use as well as major end-uses against its peers, (3) benchmarking the power usage effectiveness for the data center, which is the largest electricity consumer in this building, and (4) diagnosing HVAC equipment using detailed time-series operating data. Finally, a few energy efficiency measures were identified for retrofit, and their energy savings were estimated to be 20% of the whole-building electricity consumption. Based on the analyses, the building manager took a few steps to improve the operation of fans, chillers, and data centers, which will lead to actual energy savings. This study demonstrated that there are energy retrofit opportunities for high performance buildings and detailed measured building performance data and analytics can help identify and estimate energy savings and to inform the decision making during the retrofit process. Challenges of data collection and analytics were also discussed to shape best practice of retrofitting high performance buildings.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Craig P. Wray</style></author><author><style face="normal" font="default" size="100%">David A. Jump</style></author><author><style face="normal" font="default" size="100%">Daniel Veronica</style></author><author><style face="normal" font="default" size="100%">Christopher Farley</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of Diagnostic and Measurement and Verification Tools for Commercial Buildings</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">application programming interface</style></keyword><keyword><style  face="normal" font="default" size="100%">fault detection and diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">M&amp;V</style></keyword><keyword><style  face="normal" font="default" size="100%">Measurement and verification</style></keyword><keyword><style  face="normal" font="default" size="100%">Universal Translator</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%">09/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">California Energy Commission</style></publisher><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This research developed new measurement and verification tools and new automated fault detection and diagnosis tools, and deployed them in the Universal Translator. The Universal Translator is a tool, developed by Pacific Gas and Electric, that manages large sets of measured data from building control systems and enables off‐line analysis of building performance. There were four technical projects following the program administration tasks identified as Project 1:&lt;/p&gt;&lt;ol&gt;&lt;li&gt;Program Administration&lt;/li&gt;&lt;li&gt;Methods and Tools to Reduce the Cost of Measurement and Verification.&lt;/li&gt;&lt;li&gt;Fault Detection and Diagnostics for Commercial Heating, Ventilating, and Air‐ Conditioning Systems.&lt;/li&gt;&lt;li&gt;Test Procedures and Tools to Characterize Fan and Duct System Performance in Large Commercial Buildings.&lt;/li&gt;&lt;li&gt;Universal Translator Development: Integration of Application Programming Interface.&lt;/li&gt;&lt;/ol&gt;&lt;p&gt;Project 1 consisted of administrative tasks related to the project.&lt;/p&gt;&lt;p&gt;Project 2 addressed the need for less expensive measurement and verification tools to determine the costs and benefits of retrofits and retro‐commissioning at both the individual building level and the utility program level.&lt;/p&gt;&lt;p&gt;Project 3 extended previous work on fault detection and diagnosis to additional systems and subsystems, including dual duct heating, ventilating and air‐conditioning systems and fan‐coil terminal units.&lt;/p&gt;&lt;p&gt;Project 4 combined previous work on duct leakage and fan modeling to develop a performance assessment method for existing fan/duct systems that could also be used in the analysis of retrofit measures identified by the tools in Projects 2 and 3 using the EnergyPlus simulation program to help select the most cost‐effective package of improvements.&lt;/p&gt;&lt;p&gt;Some of the diagnostic methods and tools developed in projects 2 through 4 were incorporated in the Universal Translator via a new application programming interface that was specified, developed and tested in Project 5. Combined, these tools support analyses of energy savings produced by new construction commissioning, retro‐commissioning, improved routine operations and code compliance. The new application programming interface could also facilitate future development, testing and deployment of new diagnostic tools.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-188324</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>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shankar Earni</style></author><author><style face="normal" font="default" size="100%">Spencer Woodworth</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</style></author><author><style face="normal" font="default" size="100%">Jorge Hernandez-Maldonado</style></author><author><style face="normal" font="default" size="100%">Rongxin Yin</style></author><author><style face="normal" font="default" size="100%">Liping Wang</style></author><author><style face="normal" font="default" size="100%">Steve E. Greenberg</style></author><author><style face="normal" font="default" size="100%">John Fiegel</style></author><author><style face="normal" font="default" size="100%">Alma Rubalcava</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Monitoring-based HVAC Commissioning of an Existing Office Building for Energy Efficiency</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">benchmarking</style></keyword><keyword><style  face="normal" font="default" size="100%">commissioning</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">fault detection and diagnostics</style></keyword><keyword><style  face="normal" font="default" size="100%">functional testing</style></keyword><keyword><style  face="normal" font="default" size="100%">trend data</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%">10/2012</style></date></pub-dates></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The performance of Heating, Ventilation and Air Conditioning (HVAC) systems may fail to satisfy design expectations due to improper equipment installation, equipment degradation, sensor failures, or incorrect control sequences. Commissioning identifies and implements cost-effective operational and maintenance measures in buildings to bring them up to the design intent or optimum operation. An existing office building is used as a case study to demonstrate the process of commissioning. Building energy benchmarking tools are applied to evaluate the energy performance for screening opportunities at the whole building level. A large natural gas saving potential was indicated by the building benchmarking results. Faulty operations in the HVAC systems, such as improper operations of air-side economizers, simultaneous heating and cooling, and ineffective optimal start, were identified through trend data analyses and functional testing. The energy saving potential for each commissioning measure is quantified with a calibrated building simulation model. An actual energy saving of 10% was realized after the implementations of cost-effective measures.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-5940E</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>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%">William J. Fisk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Energy Savings Potential from the Use of Demand Control Ventilation Systems in General Office Spaces in California</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 simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">california building energy standard</style></keyword><keyword><style  face="normal" font="default" size="100%">Commercial Building Ventilation and Indoor Environmental Quality Group</style></keyword><keyword><style  face="normal" font="default" size="100%">demand controlled ventilation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy savings</style></keyword><keyword><style  face="normal" font="default" size="100%">indoor environment department</style></keyword><keyword><style  face="normal" font="default" size="100%">other</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2010</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Lawrence Berkeley National Laboratory</style></publisher><pub-location><style face="normal" font="default" size="100%">Berkeley</style></pub-location><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">117-124</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Demand controlled ventilation (DCV) was evaluated for general office spaces in California. A medium size office building meeting the prescriptive requirements of the 2008 California building energy efficiency standards (CEC 2008) was assumed in the building energy simulations performed with the EnergyPlus program to calculate the DCV energy savings potential in five typical California climates. Three design occupancy densities and two minimum ventilation rates were used as model inputs to cover a broader range of design variations. The assumed values of minimum ventilation rates in offices without DCV, based on two different measurement methods, were 81 and 28 cfm per occupant. These rates are based on the co‐author&#039;s unpublished analyses of data from EPA&#039;s survey of 100 U.S. office buildings. These minimum ventilation rates exceed the 15 to 20 cfm per person required in most ventilation standards for offices. The cost effectiveness of applying DCV in general office spaces was estimated via a life cycle cost analyses that considered system costs and energy cost reductions.&lt;/p&gt;&lt;p&gt;The results of the energy modeling indicate that the energy savings potential of DCV is largest in the desert area of California (climate zone 14), followed by Mountains (climate zone 16), Central Valley (climate zone 12), North Coast (climate zone 3), and South Coast (climate zone 6).&lt;/p&gt;&lt;p&gt;The results of the life cycle cost analysis show DCV is cost effective for office spaces if the typical minimum ventilation rates without DCV is 81 cfm per person, except at the low design occupancy of 10 people per 1000 ft&lt;sup&gt;2&lt;/sup&gt; in climate zones 3 and 6. At the low design occupancy of 10 people per 1000 ft&lt;sup&gt;2&lt;/sup&gt;, the greatest DCV life cycle cost savings is a net present value (NPV) of $0.52/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 14, followed by $0.32/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 16 and $0.19/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 12. At the medium design occupancy of 15 people per 1000 ft&lt;sup&gt;2&lt;/sup&gt;, the DCV savings are higher with a NPV $0.93/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 14, followed by $0.55/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 16, $0.46/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 12, $0.30/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 3, $0.16/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 3. At the high design occupancy of 20 people per 1000 ft&lt;sup&gt;2&lt;/sup&gt;, the DCV savings are even higher with a NPV $1.37/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 14, followed by $0.86/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 16, $0.84/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 3, $0.82/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 12, and $0.65/ft&lt;sup&gt;2&lt;/sup&gt; in climate zone 6.&lt;/p&gt;&lt;p&gt;DCV was not found to be cost effective if the typical minimum ventilation rate without DCV is 28 cfm per occupant, except at high design occupancy of 20 people per 1000 ft&lt;sup&gt;2&lt;/sup&gt; in climate zones 14 and 16.&lt;/p&gt;&lt;p&gt;Until the large uncertainties about the base case ventilation rates in offices without DCV are reduced, the case for requiring DCV in general office spaces will be a weak case.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Research Article</style></work-type><custom2><style face="normal" font="default" size="100%">LBNL-3523E</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%">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>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rüdiger Franke</style></author><author><style face="normal" font="default" size="100%">Francesco Casella</style></author><author><style face="normal" font="default" size="100%">Martin Otter</style></author><author><style face="normal" font="default" size="100%">Katrin Proelss</style></author><author><style face="normal" font="default" size="100%">Michael Sielemann</style></author><author><style face="normal" font="default" size="100%">Michael Wetter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Standardization of thermo-fluid modeling in Modelica.Fluid 1.0</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of the 7th International Modelica Conference</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">modelica</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=13</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">13</style></edition><publisher><style face="normal" font="default" size="100%">Linköping University Electronic Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Como, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">43</style></volume><isbn><style face="normal" font="default" size="100%">978-91-7393-513-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This article discusses the Modelica.Fluid library that has been included in the Modelica Standard Library 3.1. Modelica.Fluid provides interfaces and basic components for the device-oriented modeling of one dimensional thermo-fluid flow in networks containing vessels; pipes; fluid machines; valves and fittings.&lt;/p&gt;&lt;p&gt;A unique feature of Modelica.Fluid is that the component equations and the media models as well as pressure loss and heat transfer correlations are decoupled from each other. All components are implemented such that they can be used for media from the Modelica.Media library. This means that an incompressible or compressible medium; a single or a multiple substance medium with one or more phases might be used with one and the same model as long as the modeling assumptions made hold. Furthermore;&lt;/p&gt;&lt;p&gt;trace substances are supported. Modeling assumptions can be configured globally in an outer System object. This covers in particular the initialization; uni- or bi-directional flow; and dynamic or steady-state formulation of mass; energy; and momentum balance. All assumptions can be locally refined for every component.&lt;/p&gt;&lt;p&gt;While Modelica.Fluid contains a reasonable set of component models; the goal of the library is not to provide a comprehensive set of models; but rather to provide interfaces and best practices for the treatment of issues such as connector design and implementation of energy; mass and momentum balances. Applications from various domains are presented.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alberto Hernandez</style></author><author><style face="normal" font="default" size="100%">Flávio Neto</style></author><author><style face="normal" font="default" size="100%">Augusto Sanzovo Fiorelli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of Simulation Tools for Managing Buildings Energy Demand</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Building Simulation 2007</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2007</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Beijing, China</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prajesh Bhattacharya</style></author><author><style face="normal" font="default" size="100%">Wei, X.</style></author><author><style face="normal" font="default" size="100%">Andrei G. Fedorov</style></author><author><style face="normal" font="default" size="100%">Yogendra K. Joshi</style></author><author><style face="normal" font="default" size="100%">Navdeep Bajwa</style></author><author><style face="normal" font="default" size="100%">Anyuan Cao</style></author><author><style face="normal" font="default" size="100%">Pulickel Ajayan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Carbon Nanotube (CNT)-Centric Thermal Management of Future High Power Microprocessors</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE CPMT International Symposium and Exhibition on Advanced Packaging Materials</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2006</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Atlanta, GA</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%">Daniel E. Fisher</style></author><author><style face="normal" font="default" size="100%">Simon J. Rees</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling Ground Source Heat Pump Systems in a Building Energy Simulation Program (EnergyPlus)</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></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%">Chanvit Chantrasrisalai</style></author><author><style face="normal" font="default" size="100%">Daniel E. Fisher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparative Analysis of One-Dimensional Slat-Type Blind Models</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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yongcheng Jiang</style></author><author><style face="normal" font="default" size="100%">Xiufeng Pang</style></author><author><style face="normal" font="default" size="100%">Fu,Shaobo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Research of ANN Internal Model Self-tuning Control Applied in Combustion Process Control of Heating Furnace in Oil Field</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Central South University, Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">108-112</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>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vladimir Bazjanac</style></author><author><style face="normal" font="default" size="100%">James Forester</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Darko Sucic</style></author><author><style face="normal" font="default" size="100%">Peng Xu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HVAC Component Data Modeling Using Industry Foundation Classes</style></title><secondary-title><style face="normal" font="default" size="100%">System Simulation in Buildings ’02</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2002</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Liège, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Industry Foundation Classes (IFC) object data model of buildings is being developed by the International Alliance for Interoperability (IAI). The aim is to support data sharing and exchange in the building and construction industry across the life-cycle of a building.&lt;/p&gt;&lt;p&gt;This paper describes a number of aspects of a major extension of the HVAC part of the IFC data model. First is the introduction of a more generic approach for handling HVAC components. This includes type information, which corresponds to catalog data, occurrence information, which defines item-specific attributes such as location and connectivity, and performance history information, which documents the actual performance of the component instance over time. Other IFC model enhancements include an extension of the connectivity model used to specify how components forming a system can be traversed and the introduction of time-based data streams.&lt;/p&gt;&lt;p&gt;This paper includes examples of models of particular types of HVAC components, such as boilers and actuators, with all attributes included in the definitions. The paper concludes by describing the on-going process of model testing, implementation and integration into the complete IFC model and how the model can be used by software developers to support interoperability between HVAC-oriented design and analysis tools.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-51365</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arthur L. Dexter</style></author><author><style face="normal" font="default" size="100%">Richard S. Fargus</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault Detection in Air-Conditioning Systems Using A.I. Techniques</style></title><secondary-title><style face="normal" font="default" size="100%">BEP&#039;94</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><pub-location><style face="normal" font="default" size="100%">York, England</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mourad Benouarets</style></author><author><style face="normal" font="default" size="100%">Arthur L. Dexter</style></author><author><style face="normal" font="default" size="100%">Richard S. Fargus</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author><author><style face="normal" font="default" size="100%">Tim I. Salsbury</style></author><author><style face="normal" font="default" size="100%">Jonathan A. Wright</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Approaches to Fault Detection and Diagnosis in Air-Conditioning System</style></title><secondary-title><style face="normal" font="default" size="100%">System Simulation in Buildings &#039;94</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/1994</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Liège, Belgium</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>