02335nas a2200301 4500008004100000022001300041245011500054210006900169260001600238300001100254490000700265520135700272100002001629700001901649700003201668700001801700700002101718700002701739700002401766700003301790700001301823700001801836700002701854700002201881700001801903700002401921856008801945 2020 eng d a2214629600aCulture, conformity, and carbon? A multi-country analysis of heating and cooling practices in office buildings0 aCulture conformity and carbon A multicountry analysis of heating cJan-03-2020 a1013440 v613 a

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’
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.

1 aChen, Chien-fei1 aHong, Tianzhen1 ade Rubens, Gerardo, Zarazua1 aYilmaz, Selin1 aBandurski, Karol1 aBélafi, Zsófia, Deme1 aDe Simone, Marilena1 aBavaresco, Mateus, Vinícius1 aWang, Yu1 aLiu, Pei-ling1 aBarthelmes, Verena, M.1 aAdams, Jacqueline1 aD'Oca, Simona1 aPrzybylski, Łukasz uhttps://simulationresearch.lbl.gov/publications/culture-conformity-and-carbon-multi01863nas a2200205 4500008004100000245009300041210006900134260000900203520116400212653002401376653001801400653003501418653001601453653002301469100002401492700001701516700001601533700001901549856008901568 2019 eng d00aComparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs0 aComparison of MPC Formulations for Building Control under Commer c20193 a

Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formulations, from the standpoints of monthly bill reduction, load shifting, and peak demand reduction. Simulations are performed on many simplified commercial building models, with multiple TOU demand charges, and under various summer conditions. Results show that compared to energy-only MPC, the traditional method for dealing with demand charges significantly
reduces peak demand and owner bill, however, highlight a lack of load shifting capability. A proposed incremental approach
is presented, which better balances the bill components in the objective function. In the case study presented, this method
can improve monthly bill savings and increase load shifting during demand response events, while keeping a similarly low
peak demand, compared to traditional MPC methods taking into account demand charges.

10acommercial building10ademand charge10aModel predictive control (MPC)10apeak demand10atime-of-use tarrif1 aVan Cutsem, Olivier1 aKayal, Maher1 aBlum, David1 aPritoni, Marco uhttps://simulationresearch.lbl.gov/publications/comparison-mpc-formulations-building02860nas a2200253 4500008004100000022001300041245017500054210006900229260001200298300001400310490000800324520184700332653002402179653000902203653002902212653002602241100001602267700001502283700001802298700002202316700002002338700001502358856023302373 2019 eng d a0306261900aPractical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems0 aPractical factors of envelope model setup and their effects on t c02/2019 a410 - 4250 v2363 a

Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors' impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor's influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20% in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.

10abuilding simulation10ahvac10aModel predictive control10aSystem identification1 aBlum, David1 aArendt, K.1 aRivalin, Lisa1 aPiette, Mary, Ann1 aWetter, Michael1 aVeje, C.T. uhttps://linkinghub.elsevier.com/retrieve/pii/S0306261918318099https://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/plain02188nas a2200301 4500008004100000245011100041210006900152260001600221520124300237653001701480653002401497653002901521653002501550100001601575700002001591700001501611700001401626700001901640700001601659700001501675700002001690700001801710700001801728700002001746700002001766700001801786856008201804 2019 eng d00aPrototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings0 aPrototyping the BOPTEST Framework for SimulationBased Testing of aRome, Italy3 a

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.

10abenchmarking10abuilding simulation10aModel predictive control10asoftware development1 aBlum, David1 aJorissen, Filip1 aHuang, Sen1 aChen, Yan1 aArroyo, Javier1 aBenne, Kyle1 aLi, Yanfei1 aGavan, Valentin1 aRivalin, Lisa1 aHelsen, Lieve1 aVrabie, Draguna1 aWetter, Michael1 aSofos, Marina uhttps://simulationresearch.lbl.gov/publications/prototyping-boptest-framework00542nas a2200133 4500008004100000245013400041210006900175490000800244100001900252700002000271700001800291700001700309856008200326 2018 eng d00aBidirectional low temperature district energy systems with agent-based control: Performance comparison and operation optimization0 aBidirectional low temperature district energy systems with agent0 v2091 aBunning, Felix1 aWetter, Michael1 aFuchs, Marcus1 aMuller, Dirk uhttps://simulationresearch.lbl.gov/publications/bidirectional-low-temperature02422nas a2200217 4500008004100000022001400041245009700055210006900152260001200221300000900233520152700242653002301769653002201792653002801814653002201842653002501864100001901889700001901908700001801927856025901945 2018 eng d a1570-646X00aA critical review on questionnaire surveys in the field of energy-related occupant behaviour0 acritical review on questionnaire surveys in the field of energyr c07/2018 a1-213 a

Occupants perform various actions to satisfy their physical and non-physical needs in buildings. These actions greatly affect building operations and thus energy use. Clearly understanding and accurately modelling occupant behaviour in buildings are crucial to guide energy-efficient building design and operation, and to reduce the gap between design and actual energy performance of buildings. To study and understand occupant behaviour, a cross-sectional questionnaire survey is one of the most useful tools to gain insights on general behaviour patterns and drivers, and to find connections between human, social and local comfort parameters. In this study, 33 projects were reviewed from the energy-related occupant behaviour research literature that employed cross-sectional surveys or interviews for data collection from the perspective of findings, limitations and methodological challenges. This research shows that future surveys are needed to bridge the gaps in literature but they would need to encompass a multidisciplinary approach to do so as until now only environmental and engineering factors were considered in these studies. Insights from social practice theories and techniques must be acquired to deploy robust and unbiased questionnaire results, which will provide new, more comprehensive knowledge in the field, and therefore occupant behaviour could be better understood and represented in building performance simulation to support design and operation of low or net-zero energy buildings.

10abehaviour modeling10aenergy efficiency10aEnergy use in buildings10aoccupant behavior10aquestionnaire survey1 aBelafi, Zsofia1 aHong, Tianzhen1 aReith, Andras uhttp://link.springer.com/10.1007/s12053-018-9711-zhttp://link.springer.com/content/pdf/10.1007/s12053-018-9711-z.pdfhttp://link.springer.com/content/pdf/10.1007/s12053-018-9711-z.pdfhttp://link.springer.com/article/10.1007/s12053-018-9711-z/fulltext.html02513nas a2200241 4500008004100000245010500041210006900146490000800215520166100223653003101884653003301915653003201948653002201980653002102002653002502023100001802048700002502066700002402091700002702115700001902142700002602161856008402187 2018 eng d00aHuman-building interaction at work: Findings from an interdisciplinary cross-country survey in Italy0 aHumanbuilding interaction at work Findings from an interdiscipli0 v1323 a

This study presents results from an interdisciplinary survey assessing contextual and behavioral factors driving occupants' interaction with building and systems in offices located across three different Mediterranean climates in Turin (Northern), Perugia (Central), and Rende (Southern) Italy. The survey instrument is grounded in an interdisciplinary framework that bridges the gap between building physics and social science environments on the energy- and comfort-related human-building interaction in the workspace. Outcomes of the survey questionnaire provide insights into four key learning objectives: (1) individual occupant's motivational drivers regarding interaction with shared building environmental controls (such as adjustable thermostats, operable windows, blinds and shades, and artificial lighting), (2) group dynamics such as perceived social norms, attitudes, and intention to share controls, (3) occupant perception of the ease of use and knowledge of how to operate control systems, and (4) occupant-perceived comfort, satisfaction, and productivity. This study attempts to identify climatic, cultural, and socio-demographic influencing factors, as well as to establish the validity of the survey instrument and robustness of outcomes for future studies. Also, the paper aims at illustrating why and how social science insights can bring innovative knowledge into the adoption of building technologies in shared contexts, thus enhancing perceived environmental satisfaction and effectiveness of personal indoor climate control in office settings and impacting office workers' productivity and reduced operational energy costs.

10aHuman-building interaction10aindoor environmental comfort10ainterdisciplinary framework10aoccupant behavior10aoffice buildings10aquestionnaire survey1 aD'Oca, Simona1 aPisello, Anna, Laura1 aDe Simone, Marilena1 aBarthelmes, Verena, M.1 aHong, Tianzhen1 aCorgnati, Stefano, P. uhttps://simulationresearch.lbl.gov/publications/human-building-interaction-work02103nas a2200181 4500008004100000245008900041210006900130520146500199653003601664653001001700653002301710653002901733653001501762100001901777700001901796700001801815856008801833 2018 eng d00aA Library of Building Occupant Behaviour Models Represented in a Standardised Schema0 aLibrary of Building Occupant Behaviour Models Represented in a S3 a

Over the past four decades, a substantial body of literature has explored the impacts of occupant behaviour (OB) on building technologies, operation, and energy consumption. A large number of data-driven behavioural models have been developed based on field data. These models lack standardisation and consistency, leading to difficulties in applications and comparison. To address this problem, an ontology was developed using the drivers-needs-actions-systems (DNAS) framework. Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language (XML) schema, titled ‘occupant behaviour XML’ (obXML) which is a practical implementation of OB models that can be integrated into building performance simulation (BPS) programs. This paper presents a newly developed library of OB models represented in the standardised obXML schema format. This library provides ready-to-use examples for BPS users to employ more accurate occupant representation in their energy models. The library, which contains an initial effort of 52 OB models, was made publicly available for the BPS community. As part of the library development process, limitations of the obXML schema were identified and addressed, and future improvements were proposed. Authors hope that by compiling this library building, energy modellers from all over the world can enhance their BPS models by integrating more accurate and robust OB patterns.

10abuilding performance simulation10aobXML10aOccupant Behaviour10aoccupant behaviour model10aXML schema1 aBelafi, Zsofia1 aHong, Tianzhen1 aReith, Andras uhttps://simulationresearch.lbl.gov/publications/library-building-occupant-behaviour01796nas a2200157 4500008004100000245006900041210006800110260001200178520127200190100001601462700001801478700002001496700001601516700002401532856008201556 2018 eng d00aWhen Data Analytics Meet Site Operation: Benefits and Challenges0 aWhen Data Analytics Meet Site Operation Benefits and Challenges c08/20183 a

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.

1 aBlum, David1 aLin, Guanjing1 aSpears, Michael1 aPage, Janie1 aGranderson, Jessica uhttps://simulationresearch.lbl.gov/publications/when-data-analytics-meet-site02289nas a2200217 4500008004100000245009600041210006900137490000800206520155700214100002601771700001801797700002701815700002201842700001801864700002301882700001701905700002901922700002001951700001801971856008201989 2017 eng d00aDynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems0 aDynamic equationbased thermohydraulic pipe model for district he0 v1513 a

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.

1 avan der Heijde, Brahm1 aFuchs, Marcus1 aTugores, Carles, Ribas1 aSchweiger, Gerald1 aSartor, Kevin1 aBasciotti, Daniele1 aMuller, Dirk1 aNytsch-Geusen, Christoph1 aWetter, Michael1 aHelsen, Lieve uhttps://simulationresearch.lbl.gov/publications/dynamic-equation-based-thermo02095nas a2200277 4500008003900000022001300039245010600052210006900158260001200227300001400239490000800253520125200261653001701513653001801530653001701548653001501565653001301580653001501593100002601608700002001634700002101654700002001675700001901695700001801714856008501732 2017 d a0378778800aEnergy saving potential of a two-pipe system for simultaneous heating and cooling of office buildings0 aEnergy saving potential of a twopipe system for simultaneous hea c01/2017 a234 - 2470 v1343 a

This paper analyzes the performance of a novel two-pipe system that operates one water loop to simultaneously provide space heating and cooling with a water supply temperature of around 22 °C. To analyze the energy performance of the system, a simulation-based research was conducted. The two-pipe system was modelled using the equation-based Modelica modeling language in Dymola. A typical office building model was considered as the case study. Simulations were run for two construction sets of the building envelope and two conditions related to inter-zone air flows. To calculate energy savings, a conventional four-pipe system was modelled and used for comparison. The conventional system presented two separated water loops for heating and cooling with supply temperatures of 45 °C and 14 °C, respectively. Simulation results showed that the two-pipe system was able to use less energy than the four-pipe system thanks to three effects: useful heat transfer from warm to cold zones, higher free cooling potential and higher efficiency of the heat pump. In particular, the two-pipe system used approximately between 12% and 18% less total annual primary energy than the four-pipe system, depending on the simulation case considered.

10aactive beams10aenergy saving10aHVAC systems10alow-exergy10amodelica10asimulation1 aMaccarini, Alessandro1 aWetter, Michael1 aAfshari, Alireza1 aHultmark, Goran1 aBergsoe, Niels1 aVorre, Anders uhttps://simulationresearch.lbl.gov/publications/energy-saving-potential-two-pipe01392nas a2200133 4500008004100000245008600041210006900127260002700196520090500223653003501128100001601163700002001179856005901199 2017 eng d00aMPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings0 aMPCPy An OpenSource Software Platform for Model Predictive Contr aSan Franciscoc08/20173 a

Within the last decade, needs for building control systems that reduce cost, energy, or peak demand, and that facilitate building-grid integration, district-energy system optimization, and occupant interaction, while maintaining thermal comfort and indoor air quality, have come about.  Current PID and schedule-based control systems are not capable of fulfilling these needs, while Model Predictive Control (MPC) could.  Despite the critical role MPC-enabled buildings can play in future energy infrastructures, widespread adoption of MPC within the building industry has yet to occur.  To address barriers associated with system setup and configuration, this paper introduces an open-source software platform that emphasizes use of self-tuning adaptive models, usability by non-experts of MPC, and a flexible architecture that enables application across projects.

10aModel predictive control (MPC)1 aBlum, David1 aWetter, Michael uhttp://www.ibpsa.org/proceedings/BS2017/BS2017_351.pdf02042nas a2200193 4500008004100000245014000041210006900181520132600250653002201576653003601598653001801634653001501652653002201667100001901689700001701708700001901725700001801744856008601762 2017 eng d00aOccupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs0 aOccupant behavior models A critical review of implementation and3 a

Occupant behavior (OB) in buildings is a leading factor influencing energy use in buildings. Quantifying this influence requires the integration of OB models with building performance simulation (BPS). This study reviews approaches to representing and implementing OB models in today’s popular BPS programs, and discusses weaknesses and strengths of these approaches and key issues in integrating of OB models with BPS programs. Two key findings are: (1) a common data model is needed to standardize the representation of OB models, enabling their flexibility and exchange among BPS programs and user applications; the data model can be implemented using a standard syntax (e.g., in the form of XML schema), and (2) a modular software implementation of OB models, such as functional mock-up units for co-simulation, adopting the common data model, has advantages in providing a robust and interoperable integration with multiple BPS programs. Such common OB model representation and implementation approaches help standardize the input structures of OB models, enable collaborative development of a shared library of OB models, and allow for rapid and widespread integration of OB models with BPS programs to improve the simulation of occupant behavior and quantification of their impact on building performance.

10aBehavior Modeling10abuilding performance simulation10aco-simulation10adata model10aoccupant behavior1 aHong, Tianzhen1 aChen, Yixing1 aBelafi, Zsofia1 aD'Oca, Simona uhttps://simulationresearch.lbl.gov/publications/occupant-behavior-models-critical03410nas a2200409 4500008004100000245008500041210006900126260001200195520213600207653000902343653002502352653002202377653002002399653001902419653002302438653003302461653003902494653003202533653001302565653002102578100002202599700001902621700002202640700002102662700002002683700001702703700002302720700002202743700002102765700002002786700002302806700001902829700001502848700002802863700002002891856008902911 2017 eng d00aSmall and Medium Building Efficiency Toolkit and Community Demonstration Program0 aSmall and Medium Building Efficiency Toolkit and Community Demon c03/20173 a

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:

  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
  4. Detailed Retrofit Analysis which utilizes real time EnergyPlus simulations

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.

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.

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.

10aCBES10acommercial buildings10aenergy efficiency10aenergy modeling10aenergy savings10aindoor air quality10aindoor environmental quality10aoutdoor air measurement technology10aoutdoor airflow intake rate10aretrofit10aventilation rate1 aPiette, Mary, Ann1 aHong, Tianzhen1 aFisk, William, J.1 aBourassa, Norman1 aChan, Wanyu, R.1 aChen, Yixing1 aCheung, H.Y., Iris1 aHotchi, Toshifumi1 aKloss, Margarita1 aLee, Sang, Hoon1 aPrice, Phillip, N.1 aSchetrit, Oren1 aSun, Kaiyu1 aTaylor-Lange, Sarah, C.1 aZhang, Rongpeng uhttps://simulationresearch.lbl.gov/publications/small-and-medium-building-efficiency02859nas a2200229 4500008004100000022003900041245011200080210006900192260001200261300001200273490000700285520208600292653002302378653003602401653001502437653002302452653001702475100001902492700001902511700001802530856008102548 2017 eng d aPrint 1996-3599; Online 1996-874400aSmart Building Management vs. Intuitive Human Control — Lessons learnt from an office building in Hungary0 aSmart Building Management vs Intuitive Human Control Lessons lea c12/2017 a811-8280 v103 a

Smart building management and control are adopted nowadays to achieve zero-net energy use in buildings. However, without considering the human dimension, technologies alone do not necessarily guarantee high performance in buildings. An office building was designed and built according to state-of-the-art design and energy management principles in 2008. Despite the expectations of high performance, the owner was facing high utility bills and low user comfort in the building located in Budapest, Hungary. The objective of the project was to evaluate the energy performance and comfort indices of the building, to identify the causes of malfunction and to elaborate a comprehensive energy concept. Firstly, current building conditions and operation parameters were evaluated. Our investigation found that the state-of-the-art building management system was in good conditions but it was operated by building operators and occupants who are not aware of the building management practice. The energy consumption patterns of the building were simulated with energy modelling software. The baseline model was calibrated to annual measured energy consumption, using actual occupant behaviour and presence, based on results of self-reported surveys, occupancy sensors and fan-coil usage data. Realistic occupant behaviour models can capture diversity of occupant behaviour and better represent the real energy use of the building. This way our findings and the effect of our proposed improvements could be more reliable. As part of our final comprehensive energy concept, we proposed intervention measures that would increase indoor thermal comfort and decrease energy consumption of the building. A parametric study was carried out to evaluate and quantify energy, comfort and return on investment of each measure. It was found that in the best case the building could save 23% of annual energy use. Future work includes the follow-up of: occupant reactions to intervention measures, the realized energy savings, the measurement of occupant satisfaction and behavioural changes.

10abuilding operation10abuilding performance simulation10acase study10aOccupant Behaviour10aoptimization1 aBelafi, Zsofia1 aHong, Tianzhen1 aReith, Andras uhttps://simulationresearch.lbl.gov/publications/smart-building-management-vs02072nas a2200133 4500008004100000245014300041210006900184520152000253100001801773700002001791700001901811700001901830856008901849 2017 eng d00aSynthesizing building physics with social psychology: An interdisciplinary framework for context and occupant behavior in office buildings0 aSynthesizing building physics with social psychology An interdis3 a

This study introduces an interdisciplinary framework for investigating building-user interaction in office spaces. The framework is a synthesis of theories from building physics and social psychology including social cognitive theory, the theory of planned behavior, and the drivers-needs-actions-systems ontology for energy-related behaviors. The goal of the research framework is to investigate the effects of various behavioral adaptations and building controls (i.e., adjusting thermostats, operating windows, blinds and shades, and switching on/off artificial lights) to determine impacts on occupant comfort and energy-related operational costs in the office environment. This study attempts to expand state-of-the-art understanding of: (1) the environmental, personal, and behavioral drivers motivating occupants to interact with building control systems across four seasons, (2) how occupants’ intention to share controls is influenced by social-psychological variables such as attitudes, subjective norms, and perceived behavioral control in group negotiation dynamic, (3) the perceived ease of usage and knowledge of building technologies, and (4) perceived satisfaction and productivity. To ground the validation of the theoretical framework in diverse office settings and contexts at the international scale, an online survey was designed to collect cross-country responses from office occupants among 14 universities and research centers within the United States, Europe, China, and Australia.

1 aD'Oca, Simona1 aChen, Chien-Fen1 aHong, Tianzhen1 aBelafi, Zsofia uhttps://simulationresearch.lbl.gov/publications/synthesizing-building-physics-social01796nas a2200217 4500008003900000245010600039210006900145260001200214300001200226490000800238520106600246653002801312653001301340653002901353653002001382653001501402100002001417700001901437700003101456856009101487 2015 d00aEquation-based languages – A new paradigm for building energy modeling, simulation and optimization0 aEquationbased languages A new paradigm for building energy model c04/2016 a290-3000 v1173 a

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization.

We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. Exploiting the equation-based language led to 2200 times faster solution.

10aEquation-based modeling10amodelica10aMulti-physics simulation10aOptimal control10asmart grid1 aWetter, Michael1 aBonvini, Marco1 aNouidui, Thierry, Stephane uhttps://simulationresearch.lbl.gov/publications/equation-based-languages-%E2%80%93-new02234nas a2200313 4500008003900000245007900039210006900118260001200187300001200199490000700211520134300218653001401561653001501575653001801590653001501608653002401623653002901647653001501676653001301691100001701704700001901721700001301740700001401753700001301767700001501780700001501795700002201810856008801832 2014 d00aComparison of Building Energy Use Data Between the United States and China0 aComparison of Building Energy Use Data Between the United States c08/2014 a165-1750 v783 a

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.

10abuildings10acomparison10adata analysis10adata model10aEnergy benchmarking10aenergy monitoring system10aenergy use10aretrofit1 aXia, Jianjun1 aHong, Tianzhen1 aShen, Qi1 aFeng, Wei1 aYang, Le1 aIm, Piljae1 aLu, Alison1 aBhandari, Mahabir uhttps://simulationresearch.lbl.gov/publications/comparison-building-energy-use-data05291nas a2200193 4500008003900000245004400039210004400083260001200127520474700139100001904886700001404905700001504919700001704934700001304951700001304964700001504977700002204992856008305014 2013 d00aBuilding Energy Monitoring and Analysis0 aBuilding Energy Monitoring and Analysis c06/20133 a

U.S. and China are the world's top two economics. Together they consumed one-third of the world'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.

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.

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.

Key research findings were summarized as follows:

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.

1 aHong, Tianzhen1 aFeng, Wei1 aLu, Alison1 aXia, Jianjun1 aYang, Le1 aShen, Qi1 aIm, Piljae1 aBhandari, Mahabir uhttps://simulationresearch.lbl.gov/publications/building-energy-monitoring-and00650nas a2200181 4500008003900000245010300039210006900142260001200211100002100223700001800244700001500262700002100277700002100298700002100319700002100340700002300361856008400384 2013 d00aTransforming BIM to BEM: Generation of Building Geometry for the NASA Ames Sustainability Base BIM0 aTransforming BIM to BEM Generation of Building Geometry for the  c01/20131 aO'Donnell, James1 aMaile, Tobias1 aRose, Cody1 aMrazovic, Natasa1 aMorrissey, Elmer1 aRegnier, Cynthia1 aParrish, Kristen1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/transforming-bim-bem-generation01318nas a2200193 4500008003900000245006300039210006300102260002500165520071500190100002400905700001500929700001900944700002200963700002900985700002601014700002001040700001801060856004601078 2012 d00aApplication of a stochastic window use model in EnergyPlus0 aApplication of a stochastic window use model in EnergyPlus aMadison, WIc08/20123 a

Natural ventilation, used appropriately, has the potential to provide both significant HVAC energy savings, and improvements in occupant satisfaction.

Central to the development of natural ventilation models is the need to accurately represent the behavior of building occupants. The work covered in this paper describes a method of implementing a stochastic window model in EnergyPlus. Simulated window use data from three stochastic window opening models was then compared to measured window opening behavior, collected in a naturally-ventilated office in California. Recommendations regarding the selection of stochastic window use models, and their implementation in EnergyPlus, are presented.

1 aDutton, Spencer, M.1 aZhang, Hui1 aZhai, Yongchao1 aArens, Edward, A.1 aSmires, Youness, Bennani1 aBrunswick, Samuel, L.1 aKonis, Kyle, S.1 aHaves, Philip uhttps://escholarship.org/uc/item/2gm7r78301519nas a2200121 4500008003900000245011500039210006900154520103400223100001701257700001801274700002001292856008501312 2012 d00aThe Energy Saving Potential of Membrane-Based Enthalpy Recovery in Vav Systems for Commercial Office Buildings0 aEnergy Saving Potential of MembraneBased Enthalpy Recovery in Va3 a

A design tool to evaluate the heat and mass transfer effectiveness and pressure drop of a membrane-based enthalpy exchanger was developed and then used to optimize the configuration of an enthalpy exchanger for minimum pressure drop and maximum heat recovery effectiveness. Simulation was used in a parametric study to investigate the energy saving potential of the enthalpy recovery system. The case without energy recovery and air side economizer was used as a baseline. Two comparison cases for the implementation of enthalpy recovery with and without air side economizer were simulated in EnergyPlus. A case using a desiccant wheel for energy recovery was also investigated for comparison purposes. The simulation results show significant energy saving benefits from applying a low pressure drop, high effectiveness enthalpy exchanger in two US cities representing a range of humid climates. The sensitivity of the energy savings potential to pressure drop and heat and mass transfer effectivenesses is also presented.

1 aWang, Liping1 aHaves, Philip1 aBreshears, John uhttps://simulationresearch.lbl.gov/publications/energy-saving-potential-membrane00520nas a2200121 4500008004100000245011400041210006900155260003200224100001700256700001800273700002000291856008700311 2012 eng d00aThe Energy Saving Potential of Membrane-Based Enthalpy Recovery in VAV System for Commercial Office Buildings0 aEnergy Saving Potential of MembraneBased Enthalpy Recovery in VA aMadison, Wisconsinc08/20121 aWang, Liping1 aHaves, Philip1 aBreshears, John uhttps://simulationresearch.lbl.gov/publications/energy-saving-potential-membrane-001987nas a2200241 4500008004100000022001400041245008500055210006900140260001200209300001200221490000700233520120100240653003901441653002501480653002001505653001501525653003401540100001801574700002001592700002601612700001801638856008901656 2012 eng d a0360-132300aA framework for simulation-based real-time whole building performance assessment0 aframework for simulationbased realtime whole building performanc c08/2012 a100-1080 v543 a

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures. One approach to addressing this problems is to compare the predictions of an energy simulation model of the building to the measured performance and analyze significant differences to infer the presence and location of faults. This paper presents a framework that allows a comparison of building actual performance and expected performance in real time. The realization of the framework utilized the EnergyPlus, the Building Controls Virtual Test Bed (BCVTB) and the Energy Management and Control System (EMCS) was developed. An EnergyPlus model that represents expected performance of a building runs in real time and reports the predicted building performance at each time step. The BCVTB is used as the software platform to acquire relevant inputs from the EMCS through a BACnet interface and send them to the EnergyPlus and to a database for archiving. A proof-of-concept demonstration is also presented.

10abuilding controls virtual test bed10abuilding performance10aenergy modeling10aenergyplus10areal-time building simulation1 aPang, Xiufeng1 aWetter, Michael1 aBhattacharya, Prajesh1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/framework-simulation-based-real-time01431nas a2200133 4500008003900000245005600039210005300095260001200148520099100160100001701151700001801168700002101186856009001207 2012 d00aAn Improved Simple Chilled Water Cooling Coil Model0 aImproved Simple Chilled Water Cooling Coil Model c08/20123 a

The accurate prediction of cooling and dehumidification coil performance is important in model-based fault detection and in the prediction of HVAC system energy consumption for support of both design and operations. It is frequently desirable to use a simple cooling coil model that does not require detailed specification of coil geometry and material properties. The approach adopted is to match the overall UA of the coil to the rating conditions and to estimate the air-side and water-side components of the UA using correlations developed by Holmes (1982). This approach requires some geometrical information about the coil and the paper investigates the sensitivity of the overall performance prediction to uncertainties in this information, including assuming a fixed ratio of air-side to water-side UA at the rating condition. Finally, simulation results from different coil models are compared, and experimental data are used to validate the improved cooling coil model.

1 aWang, Liping1 aHaves, Philip1 aBuhl, Walter, F. uhttps://simulationresearch.lbl.gov/publications/improved-simple-chilled-water-cooling01650nas a2200157 4500008003900000245005400039210005400093260003000147520113000177100001801307700002201325700002101347700001801368700002201386856008401408 2012 d00aMapping Hvac Systems for Simulation In EnergyPlus0 aMapping Hvac Systems for Simulation In EnergyPlus aMadison, WI, USAc07/20123 a

For building energy simulation tools to be accessible to designers, tool interfaces should present a conventional view of HVAC systems to the user, and then map this view to the internal data model used in the tool. The paper outlines a process that enables design engineers to create HVAC system representations using industry standard terminology and system, icon and typological representations and convert that unified representation into the format required by the whole building energy simulation tool EnergyPlus. This paper describes each stage of the conversion process, which involves transformations between the following representations: 1) engineer's representation, 2) component connectivity representation, 3) representation in the internal data model used in the Simergy graphical user interface for EnergyPlus, and 4) EnergyPlus representation.

The paper also describes mappings between these representations and the development of a rule-based validation and assignment framework required to implement that mapping. In addition, the paper describes the implementation of this process in Simergy.

1 aMaile, Tobias1 aBasarkar, Mangesh1 aO'Donnell, James1 aHaves, Philip1 aSettlemyre, Kevin uhttps://simulationresearch.lbl.gov/publications/mapping-hvac-systems-simulation00620nas a2200169 4500008004100000245008700041210006900128260003100197100002300228700001800251700001500269700002100284700002100305700002100326700002000347856008300367 2011 eng d00aAn Assessment of the use of Building Energy Performance Simulation in Early Design0 aAssessment of the use of Building Energy Performance Simulation  aSydney, Australiac11/20111 aBazjanac, Vladimir1 aMaile, Tobias1 aRose, Cody1 aO'Donnell, James1 aMrazovic, Natasa1 aMorrissey, Elmer1 aWelle, Benjamin uhttps://simulationresearch.lbl.gov/publications/assessment-use-building-energy01404nas a2200169 4500008003900000245008200039210006900121260001200190520081400202100001801016700002601034700003101060700002001091700001701111700001801128856008801146 2011 d00aBacNet and Analog/Digital Interfaces of the Building Controls Virtual Testbed0 aBacNet and AnalogDigital Interfaces of the Building Controls Vir c11/20113 a

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.

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.

1 aHaves, Philip1 aBhattacharya, Prajesh1 aNouidui, Thierry, Stephane1 aWetter, Michael1 aLi, Zhengwei1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/bacnet-and-analogdigital-interfaces00629nas a2200169 4500008004100000245008300041210006900124260003100193300001500224100003100239700002000270700001700290700001800307700002600325700001800351856009000369 2011 eng d00aBACnet and Analog/Digital Interfaces of the Building Controls Virtual Test Bed0 aBACnet and AnalogDigital Interfaces of the Building Controls Vir aSydney, Australiac11/2011 ap. 294-3011 aNouidui, Thierry, Stephane1 aWetter, Michael1 aLi, Zhengwei1 aPang, Xiufeng1 aBhattacharya, Prajesh1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/bacnet-and-analogdigital-interfaces-000551nas a2200145 4500008004100000245008000041210006900121260002900190100002300219700001800242700002100260700001500281700002100296856008800317 2011 eng d00aData Enviroments and Processing in Sem-Automated Simulation with EnergyPlus0 aData Enviroments and Processing in SemAutomated Simulation with  aSophia Antipolis, France1 aBazjanac, Vladimir1 aMaile, Tobias1 aO'Donnell, James1 aRose, Cody1 aMrazovic, Natasa uhttps://simulationresearch.lbl.gov/publications/data-enviroments-and-processing-sem00610nas a2200157 4500008004100000245009600041210006900137260003100206100001700237700001800254700002500272700002200297700002100319700002200340856009000362 2011 eng d00aDevelopment of a user interface for the EnergyPlus whole building energy simulation program0 aDevelopment of a user interface for the EnergyPlus whole buildin aSydney, Australiac11/20111 aSee, Richard1 aHaves, Philip1 aSreekanathan, Pramod1 aBasarkar, Mangesh1 aO'Donnell, James1 aSettlemyre, Kevin uhttps://simulationresearch.lbl.gov/publications/development-user-interface-energyplus00768nas a2200241 4500008004100000245005700041210005700098260002300155653004300178653002400221653001500245653001100260653001200271653001300283653001800296653003000314100002200344700001800366700001800384700001700402700001900419856008800438 2011 eng d00aModeling and simulation of HVAC faults in EnergyPlus0 aModeling and simulation of HVAC faults in EnergyPlus aAustraliac11/201110aadvanced building software: energyplus10abuilding simulation10aenergyplus10afaults10afouling10amodeling10asensor offset10asimulation research group1 aBasarkar, Mangesh1 aHaves, Philip1 aPang, Xiufeng1 aWang, Liping1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-and-simulation-hvac-faults00466nas a2200133 4500008003900000245005800039210005800097260001200155100002200167700001800189700001700207700001900224856008900243 2011 d00aModeling and simulation of HVAC Results in EnergyPlus0 aModeling and simulation of HVAC Results in EnergyPlus c11/20111 aBasarkar, Mangesh1 aPang, Xiufeng1 aWang, Liping1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-and-simulation-hvac-results01677nas a2200181 4500008004100000245010100041210006900142260003100211300001700242520102700259100001801286700002601304700001901330700001801349700002001367700001901387856008901406 2011 eng d00aReal-time Building Energy Simulation using EnergyPlus and the Building Controls Virtual Test Bed0 aRealtime Building Energy Simulation using EnergyPlus and the Bui aSydney, Australiac11/2011 ap. 2890-28963 a

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures (Haves et al., 2001, Lee et al., 2007). To address this problem, the paper presents a simulation-based whole building performance monitoring tool that allows a comparison of building actual performance and expected performance in real time. The tool continuously acquires relevant building model input variables from existing Energy Management and Control System (EMCS). It then reports expected energy consumption as simulated of EnergyPlus. The Building Control Virtual Test Bed (BCVTB) is used as the software platform to provide data linkage between the EMCS, an EnergyPlus model, and a database. This paper describes the integrated real-time simulation environment. A proof-of-concept demonstration is also presented in the paper.

1 aPang, Xiufeng1 aBhattacharya, Prajesh1 aO'Neill, Zheng1 aHaves, Philip1 aWetter, Michael1 aBailey, Trevor uhttps://simulationresearch.lbl.gov/publications/real-time-building-energy-simulation02272nas a2200169 4500008004100000245007100041210006900112260001200181520171200193100002101905700001701926700001501943700001801958700002301976700001801999856008502017 2011 eng d00aSimModel: A domain data model for whole building energy simulation0 aSimModel A domain data model for whole building energy simulatio c10/20113 a

Many inadequacies exist within industry-standard data models as used by present-day whole-building energy simulation software. Tools such as EnergyPlus and DOE-2 use custom schema definitions (IDD and BDL respectively) as opposed to standardized schema definitions (defined in XSD, EXPRESS, etc.). Non-standard data modes lead to a requirement for application developers to develop bespoke interfaces. Such tools have proven to be error prone in their implementation – typically resulting in information loss.

This paper presents a Simulation Domain Model (SimModel) - a new interoperable XML-based data model for the building simulation domain. SimModel provides a consistent data model across all aspects of the building simulation process, thus preventing information loss. The model accounts for new simulation tool architectures, existing and future systems, components and features. In addition, it is a multi-representation model that enables integrated geometric and MEP simulation configuration data. The SimModel objects ontology moves away from tool-specific, non-standard nomenclature by implementing an industry-validated terminology aligned with Industry Foundation Classes (IFC).

The first implementation of SimModel supports translations from IDD, Open Studio IDD, gbXML and IFC. In addition, the EnergyPlus Graphic User Interface (GUI) employs SimModel as its internal data model. Ultimately, SimModel will form the basis for a new IFC Model View Definition (MVD) that will enable data exchange from HVAC Design applications to Energy Analysis applications. Extensions to SimModel could easily support other data formats and simulations (e.g. Radiance, COMFEN, etc.).

1 aO'Donnell, James1 aSee, Richard1 aRose, Cody1 aMaile, Tobias1 aBazjanac, Vladimir1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/simmodel-domain-data-model-whole01748nas a2200145 4500008003900000245008700039210006900126260003100195520120900226100001801435700002301453700002101476700001801497856008701515 2011 d00aA software tool to compare measured and simulated building energy performance data0 asoftware tool to compare measured and simulated building energy  aSydney, Australiac11/20113 a

Building energy performance is often inadequate when compared to design goals. To link design goals to actual operation one can compare measured with simulated energy performance data. Our previously developed comparison approach is the Energy Performance Comparison Methodology (EPCM), which enables the identification of performance problems based on a comparison of measured and simulated performance data. In context of this method, we developed a software tool that provides graphing and data processing capabilities of the two performance data sets. The software tool called SEE IT (Stanford Energy Efficiency Information Tool) eliminates the need for manual generation of data plots and data reformatting. SEE IT makes the generation of time series, scatter and carpet plots independent of the source of data (measured or simulated) and provides a valuable tool for comparing measurements with simulation results. SEE IT also allows assigning data points on a predefined building object hierarchy and supports different versions of simulated performance data. This paper briefly introduces the EPCM, describes the SEE IT tool and illustrates its use in the context of a building case study.

1 aMaile, Tobias1 aBazjanac, Vladimir1 aO'Donnell, James1 aGarr, Matthew uhttps://simulationresearch.lbl.gov/publications/software-tool-compare-measured-and00569nas a2200157 4500008004100000245006300041210006300104260003000167100001900197700001900216700002700235700002600262700001800288700001800306856008700324 2010 eng d00aAutomated Continuous Commissioning of Commercial Buildings0 aAutomated Continuous Commissioning of Commercial Buildings aWashington, D.C.c11/20101 aBailey, Trevor1 aO'Neill, Zheng1 aShashanka, Madhusudana1 aBhattacharya, Prajesh1 aHaves, Philip1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/automated-continuous-commissioning02003nas a2200193 4500008004100000022001800041245011000059210006900169260004100238520131100279100002201590700001801612700002001630700002001650700002401670700001501694700001801709856008201727 2010 eng d a0-918249-60-000aDevelopment and Testing of Model Predictive Control for a Campus Chilled Water Plant with Thermal Storage0 aDevelopment and Testing of Model Predictive Control for a Campus aAsilomar, California, USAbOmnipress3 a

A Model Predictive Control (MPC) implementation was developed for a university campus chilled water plant. The plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. A detailed model of the chilled water plant and simplified models of the campus buildings were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers' performance data, and calibrated using measured data collected and archived by the control system. A dynamic model of the chilled water storage tank was also developed and calibrated. A semi-empirical model was developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a MPC algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The paper describes the development and testing of the MPC implementation and discusses lessons learned and next steps in further research.

1 aCoffey, Brian, E.1 aHaves, Philip1 aWetter, Michael1 aHencey, Brandon1 aBorrelli, Francesco1 aMa, Yudong1 aBengea, Sorin uhttps://simulationresearch.lbl.gov/publications/development-and-testing-model01213nas a2200169 4500008004100000245008300041210006900124260003800193520061000231100001500841700002400856700002000880700002200900700001800922700001800940856008500958 2010 eng d00aModel Predictive Control of Thermal Energy Storage in Building Cooling Systems0 aModel Predictive Control of Thermal Energy Storage in Building C aBaltimore, Maryland, USAc06/20103 aA model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reductionin the central plant electricity cost and improvement of its efficiency.1 aMa, Yudong1 aBorrelli, Francesco1 aHencey, Brandon1 aCoffey, Brian, E.1 aBengea, Sorin1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/model-predictive-control-thermal01850nas a2200145 4500008004100000245007900041210006900120520131700189100002001506700002501526700002401551700001801575700002201593856008901615 2010 eng d00aModeling and Measurement Constraints in Fault Diagnostics for HVAC Systems0 aModeling and Measurement Constraints in Fault Diagnostics for HV3 aMany studies have shown that energy savings of five to fifteen percent are achievable in commercial buildings by detecting and correcting building faults, and optimizing building control systems. However,in spite of good progress in developing tools for determining HVAC diagnostics, methods to detect faults in HVAC systems are still generally undeveloped. Most approaches use numerical filtering or parameter estimation methods to compare data from energy meters and building sensors to predictions from mathematical or statistical models. They are effective when models are relatively accurate and data contain few errors. In this paper, we address the case where models are imperfect and data are variable, uncertain, and can contain error. We apply a Bayesian updating approach that is systematic in managing and accounting for most forms of model and data errors. The proposed method uses both knowledge of first principle modeling and empirical results to analyze the system performance within the boundaries defined by practical constraints. We demonstrate the approach by detecting faults in commercial building air handling units. We find that the limitations that exist in air handling unit diagnostics due to practical constraints can generally be effectively addressed through the proposed approach.1 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip1 aSohn, Michael, D. uhttps://simulationresearch.lbl.gov/publications/modeling-and-measurement-constraints02004nas a2200301 4500008004100000022001400041245013400055210006900189260001200258300001100270490000700281520102100288653001501309653001801324653002601342653003601368653003201404653001501436653002401451100001401475700002401489700002201513700002001535700001601555700002101571700002101592856008901613 2009 eng d a1573-198700aAnisotropy invariant Reynolds stress model of turbulence (AIRSM) and its application on attached and separated wall-bounded flows0 aAnisotropy invariant Reynolds stress model of turbulence AIRSM a c07/2009 a81-1030 v833 a

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.

10aAnisotrpoy10aInvariant map10aReynolds stress model10aReynolds-averaged Navier-Stokes10aSeparated wall-bounded flow10aTurbulence10aTurbulence modeling1 aKumar, V.1 aFrohnapfel, Bettina1 aJovanović, Jovan1 aBreuer, Michael1 aZuo, Wangda1 aHadzić, Ibrahim1 aLechner, Richard uhttps://simulationresearch.lbl.gov/publications/anisotropy-invariant-reynolds-stress00477nas a2200145 4500008004100000245004300041210003000084260002700114100001700141700001500158700002300173700002600196700001500222856009400237 2009 eng d00a“The Monitoring,” Panel: Chill-Off0 aMonitoring Panel ChillOff aSunnyvale, CAc10/20091 aNelson, Dean1 aDay, Brian1 aBell, Geoffrey, C.1 aBhattacharya, Prajesh1 aRyan, Mike uhttps://simulationresearch.lbl.gov/publications/%E2%80%9C-monitoring%E2%80%9D-panel-chill00577nas a2200157 4500008004100000245008700041210006900128260002100197100001500218700002300233700001700256700001800273700001800291700002100309856008900330 2009 eng d00aPervasive Knowledge-Based Networking for Maintenance Inspection in Smart Buildings0 aPervasive KnowledgeBased Networking for Maintenance Inspection i aBarcelona, Spain1 aMara, Paul1 aO'Sullivan, Declan1 aBrennan, Rob1 aKeane, Marcus1 aMcGlinn, Kris1 aO'Donnell, James uhttps://simulationresearch.lbl.gov/publications/pervasive-knowledge-based-networking01592nas a2200169 4500008004100000245013000041210006900171490000900240520096100249100002201210700001801232700002701250700002101277700002101298700001801319856008501337 2009 eng d00aTowards a Very Low Energy Building Stock: Modeling the US Commercial Building Stock to Support Policy and Innovation Planning0 aTowards a Very Low Energy Building Stock Modeling the US Commerc0 v37:53 a

This paper describes the origin, structure and continuing development of a model of time varying energy consumption in the US commercial building stock. The model is based on a flexible structure that disaggregates the stock into various categories (e.g. by building type, climate, vintage and life-cycle stage) and assigns attributes to each of these (e.g. floor area and energy use intensity by fuel type and end use), based on historical data and user-defined scenarios for future projections. In addition to supporting the interactive exploration of building stock dynamics, the model has been used to study the likely outcomes of specific policy and innovation scenarios targeting very low future energy consumption in the building stock. Model use has highlighted the scale of the challenge of meeting targets stated by various government and professional bodies, and the importance of considering both new construction and existing buildings.

1 aCoffey, Brian, E.1 aBorgeson, Sam1 aSelkowitz, Stephen, E.1 aApte, Joshua, S.1 aMathew, Paul, A.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/towards-very-low-energy-building00509nas a2200133 4500008004100000245007900041210006900120260001200189100002000201700002500221700002400246700001800270856008700288 2008 eng d00aApplication of Machine Learning in Fault Diagnostics of Mechanical Systems0 aApplication of Machine Learning in Fault Diagnostics of Mechanic c10/20081 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/application-machine-learning-fault01364nas a2200229 4500008004100000245006400041210006400105260000900169300001200178490000600190520067400196653002200870653001000892653001500902653002300917100001900940700002100959700001800980700002700998700002001025856008901045 2008 eng d00aComparing computer run time of building simulation programs0 aComparing computer run time of building simulation programs c2008 a210-2130 v13 a

This paper presents an approach to comparing computer run time of building simulation programs. The computing run time of a simulation program depends on several key factors, including the calculation algorithm and modeling capabilities of the program, the run period, the simulation time step, the complexity of the energy models, the run control settings, and the software and hardware configurations of the computer that is used to make the simulation runs. To demonstrate the approach, simulation runs are performed for several representative DOE-2.1E and EnergyPlus energy models. The computer run time of these energy models are then compared and analyzed.

10acomputer run time10adoe-210aenergyplus10asimulation program1 aHong, Tianzhen1 aBuhl, Walter, F.1 aHaves, Philip1 aSelkowitz, Stephen, E.1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/comparing-computer-run-time-building00443nas a2200097 4500008004100000245009700041210006900138260002500207100002600232856008700258 2008 eng d00aConvergence of IT and Facilities Real-Time and Historic Data Leads to Data Center Efficiency0 aConvergence of IT and Facilities RealTime and Historic Data Lead aOrlando, FLc05/20081 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/convergence-it-and-facilities-real00545nas a2200181 4500008004100000022001400041245005700055210005700112260001500169653002400184653002000208653001500228653001300243100001900256700002100275700001800296856004900314 2008 eng d aLBNL-822E00aEnergyPlus Analysis Capabilities for Use in Title 240 aEnergyPlus Analysis Capabilities for Use in Title 24 bLBNLc200810abuilding simulation10acode compliance10aenergyplus10atitle 241 aHong, Tianzhen1 aBuhl, Walter, F.1 aHaves, Philip uhttp://www.escholarship.org/uc/item/0z78090x00392nas a2200133 4500008004100000022003300041245003300074210003300107260001500140100001900155700002100174700001800195856004500213 2008 eng d aLBNL-1311E, CEC-500-2008-09400aEnergyPlus Run Time Analysis0 aEnergyPlus Run Time Analysis bLBNLc20081 aHong, Tianzhen1 aBuhl, Walter, F.1 aHaves, Philip uhttp://escholarship.org/uc/item/36h4m5z001212nas a2200145 4500008004100000245009100041210006900132260001200201520069300213100002000906700002500926700002400951700001800975856007300993 2008 eng d00aFault Diagnostics and Supervised Testing: How Fault Diagnostic tools can be Proactive?0 aFault Diagnostics and Supervised Testing How Fault Diagnostic to c11/20083 aThe topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.1 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttp://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=50300431nas a2200097 4500008004100000245008800041210006900129260002900198100002300227856008300250 2008 eng d00aIFC BIM-based Methodology for Semi-Automated Building Energy Performance Simulation0 aIFC BIMbased Methodology for SemiAutomated Building Energy Perfo aSantiago, Chilec07/20081 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/ifc-bim-based-methodology-semi01442nas a2200145 4500008004100000245008800041210006900129260001000198520092700208100002001135700002501155700002401180700001801204856007401222 2008 eng d00aOvercoming the Complexity of Diagnostic Problems due to Sensor Network Architecture0 aOvercoming the Complexity of Diagnostic Problems due to Sensor N c11/083 a

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system's health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

1 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttps://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=50302848nas a2200145 4500008004100000024002000041245004500061210004500106260002400151520237200175100002102547700002202568700002202590856009002612 2008 eng d aESL-HH-08-12-2700aReducing Energy Use In Florida Buildings0 aReducing Energy Use In Florida Buildings aDallas, TXc12/20083 a

The 2007 Florida Building Code (ICC, 2008) requires building designers and architects to achieve a minimum energy efficiency rating for commercial buildings located throughout Florida. Although the Florida Building Code is strict in the minimum requirements for new construction, several aspects of building construction can be further improved through careful thought and design. This report outlines several energy saving features that can be used to ensure that new buildings meet a new target goal of 85% energy use compared to the 2007 energy code in order to achieve Governor Crist's executive order to improve the energy code by 15%.

To determine if a target goal of 85% building energy use is attainable, a computer simulation study was performed to determine the energy saving features available which are, in most cases, stricter than the current Florida Building Code. The energy savings features include improvements to building envelop, fenestration, lighting and equipment, and HVAC efficiency. The imp acts of reducing outside air requirements and employing solar water heating were also investigated. Th e purpose of the energy saving features described in this document is intended to provide a simple, prescriptive method for reducing energy consumption using the methodology outlined in ASHRAE Standard 90.1 (ASHRAE, 2007).

There are two difficulties in trying to achieve savings in non-residential structures. First, there is significant energy use caused by internal loads for people and equipment and it is difficult to use the energy code to achieve savings in this area relative to a baseline. Secondly, the ASHRAE methodology uses some of the same features that are proposed for the new building, so it may be difficult to claim savings for some strategies that will produce savings such as improved ventilation controls, reduced window area, or reduced plug loads simply because the methodology applies those features to the comparison reference building.

Several measures to improve the building envelope characteristics were simulated. Simply using the selected envelope measures resulted in savings of less than 10% for all building types. However, if such measures are combined with aggressive lighting reductions and improved efficiency HVAC equipment and controls, a target savings of 15% is easily attainable.

1 aRaustad, Richard1 aBasarkar, Mangesh1 aVieira, Robin, K. uhttps://simulationresearch.lbl.gov/publications/reducing-energy-use-florida-buildings00539nas a2200121 4500008004100000245012700041210006900168260003100237100002100268700001800289700002300307856008700330 2008 eng d00aSpecification of an Information Delivery Tool to Support Optimal Holistic Environmental and Energy Management in Buildings0 aSpecification of an Information Delivery Tool to Support Optimal aBerkeley, CA, USAc07/20081 aO'Donnell, James1 aKeane, Marcus1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/specification-information-delivery00357nas a2200097 4500008004100000245004500041210004500086260001200131100002600143856009000169 2008 eng d00aUnlocking Historical Data in Critical IT0 aUnlocking Historical Data in Critical IT c12/20081 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/unlocking-historical-data-critical-it00514nas a2200121 4500008004100000245011700041210006900158260001700227100002100244700001800265700002300283856008600306 2008 eng d00aUtilisation of Whole Building Energy Simulation Output to Provide Optimum Decision Support for Building Managers0 aUtilisation of Whole Building Energy Simulation Output to Provid aBerkeley, CA1 aO'Donnell, James1 aKeane, Marcus1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/utilisation-whole-building-energy00580nas a2200157 4500008004100000245008000041210006900121260002600190100001800216700001800234700001500252700002000267700002300287700002600310856008600336 2007 eng d00aCoverage Problem for Sensors Embedded in Temperature Sensitive Environments0 aCoverage Problem for Sensors Embedded in Temperature Sensitive E aAnchorage, ALc5/20071 aSen, Arunabha1 aDas, Nibedita1 aZhou, Ling1 aShen, Bao, Hong1 aMurthy, Sudheendra1 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/coverage-problem-sensors-embedded00740nas a2200205 4500008004100000245006200041210006200103260007500165100002100240700002400261700001300285700002400298700002100322700002200343700002100365700001400386700002100400700002700421856008600448 2007 eng d00aEnergy Performance of Underfloor Air Distribution Systems0 aEnergy Performance of Underfloor Air Distribution Systems bCalifornia Energy Commission - Public Interest Energy Research Program1 aBauman, Fred, S.1 aWebster, Thomas, L.1 aJin, Hui1 aLukaschek, Wolfgang1 aBenedek, Corinne1 aArens, Edward, A.1 aLinden, Paul, F.1 aLui, Anna1 aBuhl, Walter, F.1 aDickerhoff, Darryl, J. uhttps://simulationresearch.lbl.gov/publications/energy-performance-underfloor-air00576nas a2200133 4500008004100000245013000041210006900171260002800240100002100268700001900289700002400308700002100332856008900353 2007 eng d00aGlobal Efficiency of Direct Flow Vacuum Collectors in Autonomous Solar Desiccant Cooling: Simulation and Experimental Results0 aGlobal Efficiency of Direct Flow Vacuum Collectors in Autonomous aBeijing, Chinac09/20071 aBourdoukan, Paul1 aWurtz, Etienne1 aSpérandio, Maurice1 aJoubert, Patrice uhttps://simulationresearch.lbl.gov/publications/global-efficiency-direct-flow-vacuum00529nas a2200133 4500008004100000245010800041210006900149300001200218490000800230100002200238700002600260700002400286856008500310 2006 eng d00aBrownian Motion Based Convective- Conductive Model for the Effective Thermal Conductivity of Nanofluids0 aBrownian Motion Based Convective Conductive Model for the Effect a588-5950 v1281 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/brownian-motion-based-convective00625nas a2200169 4500008004100000245009000041210006900131260002500200100002600225700001200251700002400263700002400287700001900311700001600330700002100346856008800367 2006 eng d00aCarbon Nanotube (CNT)-Centric Thermal Management of Future High Power Microprocessors0 aCarbon Nanotube CNTCentric Thermal Management of Future High Pow aAtlanta, GAc03/20061 aBhattacharya, Prajesh1 aWei, X.1 aFedorov, Andrei, G.1 aJoshi, Yogendra, K.1 aBajwa, Navdeep1 aCao, Anyuan1 aAjayan, Pulickel uhttps://simulationresearch.lbl.gov/publications/carbon-nanotube-cnt-centric-thermal00618nas a2200145 4500008004100000245013400041210006900175260002900244100001900273700002900292700002400321700001900345700002300364856008500387 2006 eng d00aA Case Study Demonstrating the Utility of Inter-Program Comparative Testing for Diagnosing Errors in Building Simulation Programs0 aCase Study Demonstrating the Utility of InterProgram Comparative aToronto, Canadac05/20061 aWeber, Andreas1 aBeausoleil-Morrison, Ian1 aGriffith, Brent, T.1 aVesanen, Teemu1 aLerson, Sébastien uhttps://simulationresearch.lbl.gov/publications/case-study-demonstrating-utility02020nas a2200265 4500008004100000245010800041210006900149260001200218300001400230490000700244520118600251653003801437653002501475653002401500100002601524700001301550700001601563700001301579700001201592700002401604700002201628700002001650700001301670856007101683 2006 eng d00aCharacterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids0 aCharacterization of the Temperature Oscillation Technique to Mea c08/2006 a2950-29560 v493 a

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.

10aTemperature oscillation technique10aThermal conductivity10athermal diffusivity1 aBhattacharya, Prajesh1 aNara, S.1 aVijayan, P.1 aTang, T.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttp://www.sciencedirect.com/science/article/pii/S001793100600144X02020nas a2200265 4500008004100000245010800041210006900149260001200218300001400230490000700244520118600251653003801437653002501475653002401500100002601524700001301550700001601563700001301579700001201592700002401604700002201628700002001650700001301670856007101683 2006 eng d00aCharacterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids0 aCharacterization of the Temperature Oscillation Technique to Mea c08/2006 a2950-29560 v493 a

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.

10aTemperature oscillation technique10aThermal conductivity10athermal diffusivity1 aBhattacharya, Prajesh1 aNara, S.1 aVijayan, P.1 aTang, T.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttp://www.sciencedirect.com/science/article/pii/S001793100600144X02129nas a2200157 4500008004100000245011200041210006900153260001600222520154600238100001901784700001601803700002201819700002101841700002101862856008801883 2006 eng d00aDehumidification Enhancement of Direct Expansion Systems through Component Augmentation of the Cooling Coil0 aDehumidification Enhancement of Direct Expansion Systems through aOrlando, FL3 a

Diverse air conditioning products with enhanced dehumidification features are being introduced to meet the increased moisture laden ventilation air requirements of ASHRAE Standard 62 in humid climates. In this evaluation, state point performance spreadsheet models for single path, mixed air packaged systems compare a conventional "off the shelf" direct expansion (DX) cooling system and its performance to systems that augment the DX coil with enhanced dehumidification components, such as heat exchangers and desiccant dehumidifiers. Using common performance metrics for comparisons at ARI rating conditions, these alternative systems define a best practice for enhanced dehumidification performance. The state point performance spreadsheet models combine available algorithms from the EnergyPlus™ simulation program for DX coils and heat exchangers with newly developed algorithms for desiccant dehumidifiers. All the models and their algorithms are applied in EnergyPlus™ for simulations of annual system cooling performance, including sensible and latent loads met, energy consumed, and humidity levels maintained, in select building types and climatic locations. Per this EnergyPlus™ analysis, these enhanced dehumidification systems present challenging decision-making tradeoffs between humidity control improvements over conventional DX systems, condensing (compressor) unit energy consumption reductions versus DX cool and reheat approaches, and fan energy use increases due to the additional component pressure drops.

1 aKosar, Douglas1 aShirey, Don1 aBasarkar, Mangesh1 aSwami, Muthasamy1 aRaustad, Richard uhttps://simulationresearch.lbl.gov/publications/dehumidification-enhancement-direct00665nas a2200169 4500008004100000245010100041210006900142260003600211100001800247700002600265700002700291700001800318700002400336700002400360700002700384856008400411 2006 eng d00aDevelopment of a Model Specification for Performance Monitoring Systems for Commercial Buildings0 aDevelopment of a Model Specification for Performance Monitoring  aPacific Grove, CA, USAc08/20061 aHaves, Philip1 aHitchcock, Robert, J.1 aGillespie, Kenneth, L.1 aBrook, Martha1 aShockman, Christine1 aDeringer, Joseph, J1 aKinney, Kristopher, L. uhttps://simulationresearch.lbl.gov/publications/development-model-specification01770nas a2200181 4500008004100000245010100041210006900142260003900211520111000250100001801360700002601378700002701404700001801431700002401449700002401473700002701497856006401524 2006 eng d00aDevelopment of a Model Specification for Performance Monitoring Systems for Commercial Buildings0 aDevelopment of a Model Specification for Performance Monitoring  aAsilomar, California, USAc08/20063 a

The paper describes the development of a model specification for performance monitoring systems for commercial buildings. The specification focuses on four key aspects of performance monitoring: performance metrics measurement system requirements data acquisition and archiving data visualization and reporting The aim is to assist building owners in specifying the extensions to their control systems that are required to provide building operators with the information needed to operate their buildings more efficiently and to provide automated diagnostic tools with the information required to detect and diagnose faults and problems that degrade energy performance. The paper reviews the potential benefits of performance monitoring, describes the specification guide and discusses briefly the ways in which it could be implemented. A prototype advanced visualization tool is also described, along with its application to performance monitoring. The paper concludes with a description of the ways in which the specification and the visualization tool are being disseminated and deployed.

1 aHaves, Philip1 aHitchcock, Robert, J.1 aGillespie, Kenneth, L.1 aBrook, Martha1 aShockman, Christine1 aDeringer, Joseph, J1 aKinney, Kristopher, L. uhttp://www.aceee.org/proceedings-paper/ss06/panel03/paper1000533nas a2200133 4500008004100000245010900041210006900150300001400219490000600233100002200239700002600261700002400287856008800311 2006 eng d00aEffect of Aggregation Kinetics on the Thermal Conductivity of Nanoscale Colloidal Solutions (Nanofluids)0 aEffect of Aggregation Kinetics on the Thermal Conductivity of Na a1529-15340 v61 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/effect-aggregation-kinetics-thermal00490nas a2200121 4500008004100000245007500041210006900116260002500185100002200210700002600232700002400258856008600282 2006 eng d00aEffect of Coloidal Chemistry on the Thermal Conductivity of Nanofluids0 aEffect of Coloidal Chemistry on the Thermal Conductivity of Nano aChicago, ILc11/20061 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/effect-coloidal-chemistry-thermal01265nas a2200169 4500008004100000245004200041210004200083260001200125300001200137490000600149520075900155100002200914700002600936700002400962700002200986856008701008 2006 eng d00aEnhanced Mass Transport in Nanofluids0 aEnhanced Mass Transport in Nanofluids c03/2006 a419-4230 v63 a

Thermal conductivity enhancement in nanofluids, which are liquids containing suspended nanoparticles, has been attributed to localized convection arising from the nanoparticles' Brownian motion. Because convection and mass transfer are similar processes, the objective here is to visualize dye diffusion in nanofluids. It is observed that dye diffuses faster in nanofluids compared to that in water, with a peak enhancement at a nanoparticle volume fraction, φ, of 0.5%. A possible change in the slope of thermal conductivity enhancement at that same φ signifies that convection becomes less important at higher φ. The enhanced mass transfer in nanofluids can be utilized to improve diffusion in microfluidic devices.

1 aKrishnamurthy, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/enhanced-mass-transport-nanofluids00517nas a2200109 4500008004100000245013500041210006900176260003200245100001900277700002100296856009000317 2006 eng d00aEvaluation of Methods for Determining Demand-Limiting Setpoint Trajectories in Commercial Buildings Using Short-Term Data Analysis0 aEvaluation of Methods for Determining DemandLimiting Setpoint Tr aCambridge, MA, USAc08/20061 aLee, Kyoung-ho1 aBraun, James, E. uhttps://simulationresearch.lbl.gov/publications/evaluation-methods-determining-demand00350nas a2200109 4500008004100000245002900041210002900070260002400099100002200123700002100145856007400166 2006 eng d00aIFC to CONTAM Translator0 aIFC to CONTAM Translator aBoston, MAc08/20061 aBasarkar, Mangesh1 aSwami, Muthasamy uhttps://simulationresearch.lbl.gov/publications/ifc-contam-translator00549nas a2200145 4500008004100000245006900041210006800110260003200178100001900210700002100229700002100250700001700271700002600288856008900314 2006 eng d00aUsing EnergyPlus for California Title-24 Compliance Calculations0 aUsing EnergyPlus for California Title24 Compliance Calculations aCambridge, MA, USAc08/20061 aHuang, Yu, Joe1 aBourassa, Norman1 aBuhl, Walter, F.1 aErdem, Ender1 aHitchcock, Robert, J. uhttps://simulationresearch.lbl.gov/publications/using-energyplus-california-title-2401595nas a2200169 4500008004100000050001500041245006900056210006800125260002700193520101300220100001901233700002101252700002101273700001701294700002601311856008801337 2006 eng d aLBNL-6152700aUsing EnergyPlus for California Title-24 compliance calculations0 aUsing EnergyPlus for California Title24 compliance calculations aCambridge, MAc08/20063 a

For the past decade, the non-residential portion of California's Title-24 building energy standard has relied on DOE-2.1E as the reference computer simulation program for development as well as compliance. However, starting in 2004, the California Energy Commission has been evaluating the possible use of EnergyPlus as the reference program in future revisions of Title-24. As part of this evaluation, the authors converted the Alternate Compliance Method (ACM) certification test suite of 150 DOE-2 files to EnergyPlus, and made parallel DOE-2 and EnergyPlus runs for this extensive set of test cases. A customized version of DOE-2.1E named doe2ep was developed to automate the conversion process. This paper describes this conversion process, including the difficulties in establishing an apples-to-apples comparison between the two programs, and summarizes how the DOE-2 and EnergyPlus results compare for the ACM test cases.

1 aHuang, Yu, Joe1 aBourassa, Norman1 aBuhl, Walter, F.1 aErdem, Ender1 aHitchcock, Robert, J. uhttps://simulationresearch.lbl.gov/publications/using-energyplus-california-title-000481nas a2200121 4500008004100000245006800041210006800109260002500177100002600202700002400228700002200252856008500274 2005 eng d00aComputational Analysis of the Colloidal Stability of Nanofluids0 aComputational Analysis of the Colloidal Stability of Nanofluids aOrlando, FLc11/20051 aBhattacharya, Prajesh1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/computational-analysis-colloidal00511nas a2200121 4500008004100000245008500041210006900126260003000195100003500225700002100260700001800281856009000299 2005 eng d00aDesign of the Natural Ventilation System for the New San Diego Children's Museum0 aDesign of the Natural Ventilation System for the New San Diego C aMontreal, Canadac08/20051 ada Graça, Guilherme, Carrilho1 aLinden, Paul, F.1 aBrook, Martha uhttps://simulationresearch.lbl.gov/publications/design-natural-ventilation-system-new00634nas a2200181 4500008004100000245008100041210006900122260003100191100001600222700002600238700001300264700001200277700002400289700002200313700002000335700001300355856008400368 2005 eng d00aEffect of Particle Material on the Static Thermal Conductivity of Nanofluids0 aEffect of Particle Material on the Static Thermal Conductivity o aSan Francisco, CAc07/20051 aVijayan, P.1 aBhattacharya, Prajesh1 aNara, S.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttps://simulationresearch.lbl.gov/publications/effect-particle-material-static01543nas a2200217 4500008004100000020001800041245013200059210006900191260003100260520078000291100001301071700002601084700001601110700001201126700001801138700002401156700002201180700002001202700001701222856008601239 2005 eng d a0-7918-4221-500aExperimental Determination of the Effect of Varying Base Fluid and Temperature on the Static Thermal Conductivity of Nanofluids0 aExperimental Determination of the Effect of Varying Base Fluid a aOrlando, FLbASMEc11/20053 a

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.

1 aNara, S.1 aBhattacharya, Prajesh1 aVijayan, P.1 aLai, W.1 aRosenthal, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, Jinlin uhttps://simulationresearch.lbl.gov/publications/experimental-determination-effect00465nas a2200121 4500008004100000245007100041210006900112490000700181100002200188700002600210700002400236856008300260 2005 eng d00aThermal Conductivity of Nanoscale Colloidal Solutions (Nanofluids)0 aThermal Conductivity of Nanoscale Colloidal Solutions Nanofluids0 v941 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/thermal-conductivity-nanoscale01573nas a2200217 4500008004100000245009500041210006900136260001200205300001600217490000700233520084100240653001901081653002101100653004601121100002601167700001501193700002001208700002401228700002201252856008101274 2004 eng d00aBrownian Dynamics Simulation to Determine the Effective Thermal Conductivity of Nanofluids0 aBrownian Dynamics Simulation to Determine the Effective Thermal  c06/2004 a6492–64940 v953 a

A nanofluid is a fluid containing suspended solid particles, with sizes on the order of nanometers. Normally, nanofluids have higher thermal conductivities than their base fluids. Therefore, it is of interest to predict the effective thermal conductivity of such a nanofluid under different conditions, especially since only limited experimental data are available. We have developed a technique to compute the effective thermal conductivity of a nanofluid using Brownian dynamics simulation, which has the advantage of being computationally less expensive than molecular dynamics, and have coupled that with the equilibrium Green-Kubo method. By comparing the results of our calculation with the available experimental data, we show that our technique predicts the thermal conductivity of nanofluids to a good level of accuracy.

10acomplex fluids10aDisperse systems10aThermal conduction in nonmetallic liquids1 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/brownian-dynamics-simulation00461nas a2200121 4500008004100000050001700041245009000058210006900148300001200217490000700229100002300236856008000259 2004 eng d aLBNL/PUB-90500aBuilding energy performance simulation as part of interoperable software environments0 aBuilding energy performance simulation as part of interoperable  a879-8830 v391 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/building-energy-performance00455nas a2200121 4500008004100000245009000041210006900131260000900200300001200209490000700221100002300228856008200251 2004 eng d00aBuilding Energy Performance Simulation as Part of Interoperable Software Environments0 aBuilding Energy Performance Simulation as Part of Interoperable  c2004 a879-8830 v391 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/building-energy-performance-000508nas a2200133 4500008004100000245006300041210006200104260003600166100002100202700002100223700001800244700002300262856008900285 2004 eng d00aBuildingPI: A Future Tool for Building Life Cycle Analysis0 aBuildingPI A Future Tool for Building Life Cycle Analysis aBoulder, Colorado, USAc08/20041 aO'Donnell, James1 aMorrissey, Elmer1 aKeane, Marcus1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/buildingpi-future-tool-building-life00504nas a2200121 4500008004100000245009000041210006900131260002600200100002600226700002400252700002200276856008400298 2004 eng d00aDetermining the Effective Viscosity of a Nanofluid Using Brownian Dynamics Simulation0 aDetermining the Effective Viscosity of a Nanofluid Using Brownia aHonolulu, HIc03/20041 aBhattacharya, Prajesh1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/determining-effective-viscosity00835nas a2200277 4500008004100000245002600041210002500067260003600092100002300128700002200151700002500173700003000198700002300228700002400251700002400275700002100299700001900320700002600339700001800365700002300383700002100406700002400427700002100451700001500472856007000487 2004 eng d00aEnergyPlus: An Update0 aEnergyPlus An Update aBoulder, Colorado, USAc08/20041 aCrawley, Drury, B.1 aLawrie, Linda, K.1 aPedersen, Curtis, O.1 aWinkelmann, Frederick, C.1 aWitte, Michael, J.1 aStrand, Richard, K.1 aLiesen, Richard, J.1 aBuhl, Walter, F.1 aHuang, Yu, Joe1 aHenninger, Robert, H.1 aGlazer, Jason1 aFisher, Daniel, E.1 aShirley, Don, B.1 aGriffith, Brent, T.1 aEllis, Peter, G.1 aGu, Lixing uhttps://simulationresearch.lbl.gov/publications/energyplus-update00745nas a2200181 4500008004100000245019400041210006900235260002500304100002600329700001600355700001300371700001300384700002400397700002200421700001300443700002000456856008700476 2004 eng d00aEvaluation of the Temperature Oscillation Technique to Calculate Thermal Conductivity of Water and Systematic Measurement of the Thermal Conductivity of Aluminum Oxide – Water Nanofluiids0 aEvaluation of the Temperature Oscillation Technique to Calculate aAnaheim, CAc11/20041 aBhattacharya, Prajesh1 aVijayan, P.1 aTang, T.1 aNara, S.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aWang, J.1 aSong, David, W. uhttps://simulationresearch.lbl.gov/publications/evaluation-temperature-oscillation00480nas a2200109 4500008004100000245009600041210006900137260003600206100002300242700001800265856008700283 2004 eng d00aIFC HVAC Interface to EnergyPlus: A Case of Expanded Interoperability for Energy Simulation0 aIFC HVAC Interface to EnergyPlus A Case of Expanded Interoperabi aBoulder, Colorado, USAc08/20041 aBazjanac, Vladimir1 aMaile, Tobias uhttps://simulationresearch.lbl.gov/publications/ifc-hvac-interface-energyplus-case00501nas a2200121 4500008004100000050001700041245009700058210006900155260002500224100002300249700001800272856008900290 2004 eng d aLBNL/PUB-90700aIFC HVAC interface to EnergyPlus - A case of expanded interoperability for energy simulation0 aIFC HVAC interface to EnergyPlus A case of expanded interoperabi aBoulder, COc08/20041 aBazjanac, Vladimir1 aMaile, Tobias uhttps://simulationresearch.lbl.gov/publications/ifc-hvac-interface-energyplus-case-000461nas a2200133 4500008004100000245005300041210005200094260001200146100001600158700002400174700002000198700002600218856008300244 2004 eng d00aNumerical Tools For Particle- Fluid Interactions0 aNumerical Tools For Particle Fluid Interactions c02/20041 aCalhoun, R.1 aPhelan, Patrick, E.1 aYadav, Ajay, K.1 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/numerical-tools-particle-fluid02097nas a2200157 4500008003900000245009300039210006900132260003100201520151300232653003401745100001301779700001801792700002201810700002101832856008601853 2004 d00aPeak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building0 aPeak Demand Reduction from PreCooling with Zone Temperature Rese aPacific Grove, CAc08/20043 a

The objective of this study was to demonstrate the potential for reducing peak-period electrical demand in moderate-weight commercial buildings by modifying the control of the HVAC system. An 80,000 ft2 office building with a medium-weight building structure and high window-to-wall ratio was used for a case study in which zone temperature set-points were adjusted prior to and during occupancy. HVAC performance data and zone temperatures were recorded using the building control system. Additional operative temperature sensors for selected zones and power meters for the chillers and the AHU fans were installed for the study. An energy performance baseline was constructed from data collected during normal operation. Two strategies for demand shifting using the building thermal mass were then programmed in the control system and implemented progressively over a period of one month. It was found that a simple demand limiting strategy performed well in this building. This strategy involved maintaining zone temperatures at the lower end of the comfort region during the occupied period up until 2 pm. Starting at 2 pm, the zone temperatures were allowed to float to the high end of the comfort region. With this strategy, the chiller power was reduced by 80-100% (1 - 2.3 W/ft2) during normal peak hours from 2 - 5 pm, without causing any thermal comfort complaints. The effects on the demand from 2 - 5 pm of the inclusion of pre-cooling prior to occupancy are unclear.

10ademand shifting (pre-cooling)1 aXu, Peng1 aHaves, Philip1 aPiette, Mary, Ann1 aBraun, James, E. uhttps://simulationresearch.lbl.gov/publications/peak-demand-reduction-pre-cooling00506nas a2200121 4500008004100000245008400041210006900125260003600194100002100230700002400251700002600275856008300301 2004 eng d00aSimulation of Tubular Daylighting Devices and Daylighting Shelves in EnergyPlus0 aSimulation of Tubular Daylighting Devices and Daylighting Shelve aBoulder, Colorado, USAc08/20041 aEllis, Peter, G.1 aStrand, Richard, K.1 aBaumgartner, Kurt, T. uhttps://simulationresearch.lbl.gov/publications/simulation-tubular-daylighting00587nas a2200133 4500008004100000245013700041210006900178260003600247100002100283700002100304700001800325700002300343856008700366 2004 eng d00aSpecification and Implementation of IFC Based Performance Metrics to Support Building Life Cycle Assessment of Hybrid Energy Systems0 aSpecification and Implementation of IFC Based Performance Metric aBoulder, Colorado, USAc08/20041 aMorrissey, Elmer1 aO'Donnell, James1 aKeane, Marcus1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/specification-and-implementation-000605nas a2200145 4500008004100000050001700041245013700058210006900195260002500264100002100289700002100310700001800331700002300349856008700372 2004 eng d aLBNL/PUB-90600aSpecification and Implementation of IFC Based Performance Metrics to Support Building Life Cycle Assessment of Hybrid Energy Systems0 aSpecification and Implementation of IFC Based Performance Metric aBoulder, COc08/20041 aMorrissey, Elmer1 aO'Donnell, James1 aKeane, Marcus1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/specification-and-implementation-100589nas a2200133 4500008004100000245013700041210006900178260003600247100002100283700002100304700001800325700002300343856008900366 2004 eng d00aSpecification and Implementation of IFC-Based Performance Metrics to Support Building Life Cycle Assessment of Hybrid Energy Systems0 aSpecification and Implementation of IFCBased Performance Metrics aBoulder, Colorado, USAc08/20041 aMorrissey, Elmer1 aO'Donnell, James1 aKeane, Marcus1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/specification-and-implementation-ifc00567nas a2200133 4500008004100000245011600041210006900157260003600226100002100262700002100283700001800304700002300322856008800345 2004 eng d00aSpecification of IFC Based Performance Metrics to Support Building Life Cycle Analysis of Hybrid Energy Systems0 aSpecification of IFC Based Performance Metrics to Support Buildi aBoulder, Colorado, USAc08/20041 aMorrissey, Elmer1 aO'Donnell, James1 aKeane, Marcus1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/specification-ifc-based-performance01157nas a2200121 4500008004100000050001500041245007900056210006900135260003000204520069600234100002300930856008200953 2004 eng d aLBNL-5607200aVirtual Building Environments - Applying Information Modeling to Buildings0 aVirtual Building Environments Applying Information Modeling to B aIstanbul, Turkeyc09/20043 a

A Virtual Building Environment (VBE) is a place where building industry project staffs can get help in creating Building Information Models (BIM) and in the use of virtual buildings. It consists of a group of industry software that is operated by industry experts who are also experts in the use of that software. The purpose of a VBE is to facilitate expert use of appropriate software applications in conjunction with each other to efficiently support multidisciplinary work. This paper defines BIM and virtual buildings, and describes VBE objectives, set-up and characteristics of operation. It informs about the VBE Initiative and the benefits from a couple of early VBE projects.

1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/virtual-building-environments00573nas a2200145 4500008004100000245010100041210006900142260002700211100002600238700001500264700002000279700002400299700002200323856008200345 2003 eng d00aDetermining the Effective Thermal Conductivity of a Nanofluid Using Brownian Dynamics Simulation0 aDetermining the Effective Thermal Conductivity of a Nanofluid Us aLas Vegas, NVc07/20031 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/determining-effective-thermal00504nas a2200133 4500008004100000245008500041210006900126300001200195490000700207100002500214700001800239700002500257856008800282 2003 eng d00aField Testing Model-Based Condition Monitoring on a HVAC Cooling Coil Sub-System0 aField Testing ModelBased Condition Monitoring on a HVAC Cooling  a103-1160 v241 aBuswell, Richard, A.1 aHaves, Philip1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/field-testing-model-based-condition00488nas a2200121 4500008004100000050001700041245008400058210006900142260003600211490000600247100002300253856009000276 2003 eng d aLBNL/PUB-90800aImproving building energy performance simulation with software interoperability0 aImproving building energy performance simulation with software i aEindhoven, Netherlandsc08/20030 v11 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/improving-building-energy-performance01794nas a2200133 4500008004100000245009600041210006900137260003900206520127100245100002101516700001801537700001901555856008601574 2002 eng d00aA Computer Simulation Appraisal of Non-Residential Low Energy Cooling Systems in California0 aComputer Simulation Appraisal of NonResidential Low Energy Cooli aAsilomar, California, USAc08/20023 a

An appraisal of the potential performance of different Low Energy Cooling (LEC) systems in nonresidential buildings in California is being conducted using computer simulation. The paper presents results from the first phase of the study, which addressed the systems that can be modeled, with the DOE-2.1E simulation program.

The following LEC technologies were simulated as variants of a conventional variable-air-volume system with vapor compression cooling and mixing ventilation in the occupied spaces:

Results are presented for four populous climates, represented by Oakland, Sacramento, Pasadena and San Diego. The greatest energy savings are obtained from a combination of displacement ventilation and air-side indirect/direct evaporative pre-cooling. Cool beam systems have the lowest peak demand but do not reduce energy consumption significantly because the reduction in fan energy is offset by a reduction in air-side free cooling. Overall, the results indicate significant opportunities for LEC technologies to reduce energy consumption and demand in non-residential new construction and retrofit.

1 aBourassa, Norman1 aHaves, Philip1 aHuang, Yu, Joe uhttps://simulationresearch.lbl.gov/publications/computer-simulation-appraisal-non01937nas a2200169 4500008004100000024001500041245006700056210006700123260002900190520137000219100002301589700002001612700001801632700001701650700001301667856008701680 2002 eng d aLBNL-5136500aHVAC Component Data Modeling Using Industry Foundation Classes0 aHVAC Component Data Modeling Using Industry Foundation Classes aLiège, Belgiumc12/20023 a

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.

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.

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.

1 aBazjanac, Vladimir1 aForester, James1 aHaves, Philip1 aSucic, Darko1 aXu, Peng uhttps://simulationresearch.lbl.gov/publications/hvac-component-data-modeling-using00472nas a2200109 4500008004100000245009400041210006900135260002800204100002600232700002400258856008000282 2002 eng d00aModeling the Behavior of F1-ATPase Biomolecular Motors Using Brownian Dynamics Simulation0 aModeling the Behavior of F1ATPase Biomolecular Motors Using Brow aScottsdale, AZc09/20021 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/modeling-behavior-f1-atpase00551nas a2200133 4500008004100000245010500041210006900146260002900215100002500244700001800269700002200287700002500309856008300334 2002 eng d00aNon-Linear Recursive Parameter Estimation Applied to Fault Detection and Diagnosis in Real Buildings0 aNonLinear Recursive Parameter Estimation Applied to Fault Detect aLiège, Belgiumc12/20021 aBuswell, Richard, A.1 aHaves, Philip1 aSalsbury, Tim, I.1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/non-linear-recursive-parameter00482nas a2200109 4500008004100000245010100041210006900142260002600211100002600237700002400263856008500287 2002 eng d00aUnderstanding the Behavior of an F1-ATPase Biomolecular Motor Using Brownian Dynamics Simulation0 aUnderstanding the Behavior of an F1ATPase Biomolecular Motor Usi aBerkeley, CAc06/20021 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/understanding-behavior-f1-atpase01168nas a2200169 4500008004100000245007600041210006900117260001200186300001200198490000700210520062300217653002400840100001900864700001900883700001500902856008100917 2000 eng d00aBuilding simulation: an overview of development and information sources0 aBuilding simulation an overview of development and information s c05/2000 a347-3610 v353 a

We review the state-of-the-art on the development and application of computer-aided building simulation by addressing some crucial questions in the field. Although the answers are not intended to be comprehensive, they are sufficiently varied to provide an overview ranging from the historical and technical development to choosing a suitable simulation program and performing building simulation. Popular icons of major interested agencies and simulation tools and key information sources are highlighted. Future trends in the design and operation of energy-efficient ‘green' buildings are briefly described.

10abuilding simulation1 aHong, Tianzhen1 aChou, Siaw, K.1 aBong, T.Y. uhttps://simulationresearch.lbl.gov/publications/building-simulation-overview01723nas a2200217 4500008004100000245005700041210005500098260001200153300001200165490000700177520108800184653002401272653001501296653001001311653002601321653001701347100001901364700001901383700001501402856008801417 1999 eng d00aA design day for building load and energy estimation0 adesign day for building load and energy estimation c07/1999 a469-4770 v343 a

We describe how a design day for building energy performance simulation can be selected from a typical meteorological year of a location. The advantages of the design day weather file are its simplicity and flexibility in use with simulation programs. The design day is selected using a weather parameter comprising the daily average dry bulb temperature and total solar insolation. The selection criterion addresses the balance between the need to minimise the part-load performance of the air-conditioning systems and plants and the number of hours of load not met. To validate the versatility of the design day weather file, we compare simulation results of the peak load and load profile of a building obtained from the DOE-2.1E code and a specially developed load estimation program, PEAKLOAD. PEAKLOAD is developed using the transfer function method and ASHRAE databases. Comparative results are in good agreement, indicating that a design day thus selected can be used when quick answers are required and simulations using a TMY file cannot be easily done or justified.

10abuilding simulation10adesign day10adoe-210apeak load calculation10aweather data1 aHong, Tianzhen1 aChou, Siaw, K.1 aBong, T.Y. uhttps://simulationresearch.lbl.gov/publications/design-day-building-load-and-energy00471nas a2200121 4500008004100000245006400041210006100105260003100166100002500197700001800222700002200240856008700262 1997 eng d00aA Model-Based Approach to the Commissioning of HVAC Systems0 aModelBased Approach to the Commissioning of HVAC Systems aBrussles, Belgiumc08/19971 aBuswell, Richard, A.1 aHaves, Philip1 aSalsbury, Tim, I. uhttps://simulationresearch.lbl.gov/publications/model-based-approach-commissioning00448nas a2200133 4500008004100000245004600041210004600087300001200133490000700145100002400152700002200176700002600198856009000224 1995 eng d00aNanofluids for Heat Transfer Applications0 aNanofluids for Heat Transfer Applications a255-2750 v141 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/nanofluids-heat-transfer-applications00600nas a2200157 4500008004100000245008700041210006900128260002900197100002300226700002300249700002400272700001800296700002200314700002500336856008100361 1994 eng d00aModel-Based Approaches to Fault Detection and Diagnosis in Air-Conditioning System0 aModelBased Approaches to Fault Detection and Diagnosis in AirCon aLiège, Belgiumc12/19941 aBenouarets, Mourad1 aDexter, Arthur, L.1 aFargus, Richard, S.1 aHaves, Philip1 aSalsbury, Tim, I.1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/model-based-approaches-fault00380nas a2200121 4500008004100000245003000041210003000071260003100101100001600132700001800148700001700166856007500183 1992 eng d00aImpacts of Climate Change0 aImpacts of Climate Change aSolihull, Englandc05/19921 aHulme, Mike1 aHaves, Philip1 aBoardman, B. uhttps://simulationresearch.lbl.gov/publications/impacts-climate-change00555nas a2200169 4500008003900000245004100039210004100080260003500121100002500156700002200181700002400203700002200227700002000249700002500269700003000294856006100324 1989 d00aThermal Energy Storage System Sizing0 aThermal Energy Storage System Sizing aVancouver, BC, Canadac01/19891 aDumortier, Dominique1 aKammerud, Ron, C.1 aBirdsall, Bruce, E.1 aAndersson, Brandt1 aEto, Joseph, H.1 aCarroll, William, L.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1989/BS89_357_362.pdf00671nas a2200193 4500008003900000245006700039210006200106260004300168490000800211100002400219700002100243700002400264700001700288700001600305700002200321700002100343700003000364856008300394 1985 d00aThe DOE-2 Computer Program for Thermal Simulation of Buildings0 aDOE2 Computer Program for Thermal Simulation of Buildings bAmerican Institute of Physicsc01/19850 v1351 aBirdsall, Bruce, E.1 aBuhl, Walter, F.1 aCurtis, Richard, B.1 aErdem, Ender1 aEto, Joseph1 aHirsch, James, J.1 aOlson, Karen, H.1 aWinkelmann, Frederick, C. uhttps://simulationresearch.lbl.gov/publications/doe-2-computer-program-thermal00539nas a2200145 4500008003900000245005700039210005500096260007100151100002100222700001700243700002000260700002200280700003000302856006100332 1985 d00aNew Features of the DOE-2.1c Energy Analysis Program0 aNew Features of the DOE21c Energy Analysis Program bInternational Building Performance Simulation Associationc01/19851 aBuhl, Walter, F.1 aErdem, Ender1 aEto, Joseph, H.1 aHirsch, James, J.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1985/BS85_195_200.pdf00595nas a2200181 4500008003900000245004700039210004200086260002300128100002400151700002400175700002100199700001700220700002000237700002200257700002100279700003000300856008300330 1984 d00aThe DOE-2 Building Energy Analysis Program0 aDOE2 Building Energy Analysis Program aSingaporec05/19841 aCurtis, Richard, B.1 aBirdsall, Bruce, E.1 aBuhl, Walter, F.1 aErdem, Ender1 aEto, Joseph, H.1 aHirsch, James, J.1 aOlson, Karen, H.1 aWinkelmann, Frederick, C. uhttps://simulationresearch.lbl.gov/publications/doe-2-building-energy-analysis00578nas a2200157 4500008004100000022002900041245004800070210004800118260008900166100001500255700001800270700001600288700001800304700001600322856008200338 1981 eng d a0895530333 978089553033200aHeat Loss Rates from Wetted Tilted Surfaces0 aHeat Loss Rates from Wetted Tilted Surfaces aMiami Beach, FLbAmerican Section of the International Solar Energy Societyc11/19811 aHaines, R.1 aHaves, Philip1 aVollink, D.1 aBowen, Arthur1 aClark, Gene uhttps://simulationresearch.lbl.gov/publications/heat-loss-rates-wetted-tilted00416nas a2200121 4500008004100000245004800041210004800089260002500137100001800162700001500180700001600195856008300211 1980 eng d00aHeat Transfer in Passively Cooled Buildings0 aHeat Transfer in Passively Cooled Buildings aOrlando, FLc07/19801 aHaves, Philip1 aBently, D.1 aClark, Gene uhttps://simulationresearch.lbl.gov/publications/heat-transfer-passively-cooled00825nas a2200253 4500008004100000245008000041210007100121260001200192300001200204490000800216653002500224653002600249653001200275653001900287653003000306653001800336653002200354653002600376100002100402700001800423700002300441700002000464856008700484 1976 eng d00aThe Radio Structure of a Sample of 101 Quasars from the Parkes ±4º Survey0 aRadio Structure of a Sample of 101 Quasars from the Parkes ±4º S c08/1976 a275-3060 v17610aAngular Distribution10aAstronomical Catalogs10aQuasars10aRadio Emission10aRadio Sources (Astronomy)10aRadio Spectra10aSpectrum Analysis10aUltrahigh Frequencies1 aBentley, Michael1 aHaves, Philip1 aSpencer, Ralph, E.1 aStannard, David uhttps://simulationresearch.lbl.gov/publications/radio-structure-sample-101-quasars00495nas a2200133 4500008004100000245007100041210006900112490000800181100002100189700001800210700002300228700002000251856009000271 1975 eng d00aHigh Resolution Observations of Extended Radio Sources at 1666 MHz0 aHigh Resolution Observations of Extended Radio Sources at 1666 M0 v1731 aBentley, Michael1 aHaves, Philip1 aSpencer, Ralph, E.1 aStannard, David uhttps://simulationresearch.lbl.gov/publications/high-resolution-observations-extended00504nas a2200181 4500008004100000245002700041210002600068260001200094300001200106490000800118100002200126700002100148700001800169700002100187700002300208700002000231856007100251 1974 eng d00aQSOs of High Redshift?0 aQSOs of High Redshift c11/1974 a209-2100 v2521 aBrowne, Ian, W.A.1 aBentley, Michael1 aHaves, Philip1 aMcEwan, Neil, J.1 aSpencer, Ralph, E.1 aStannard, David uhttps://simulationresearch.lbl.gov/publications/qsos-high-redshift