01980nas a2200277 4500008004100000022001300041245009800054210006900152260001200221300001400233490000800247520110300255653002201358653004301380653002401423653002601447653003401473653002101507653002001528100001401548700002201562700001601584700001601600700001901616856006701635 2019 eng d a0360132300aThe Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes0 aSqueaky wheel Machine learning for anomaly detection in subjecti c03/2019 a219 - 2270 v1513 a

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants' votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

10aanomaly detection10aASHRAE global thermal comfort database10aK-nearest neighbors10aMultivariate Gaussian10aOccupancy responsive controls10aSubjective votes10athermal comfort1 aWang, Zhe1 aParkinson, Thomas1 aLi, Peixian1 aLin, Borong1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930086101432nas a2200121 4500008004100000245005900041210005900100520100900159100002001168700002001188700001801208856008401226 2018 eng d00aSimplifications for hydronic system models in Modelica0 aSimplifications for hydronic system models in Modelica3 a

Building systems and their heating, ventilation and air conditioning ow networks, are becoming increasingly complex. Some building energy simulation tools simulate these ow networks using pressure drop equations. These ow network models typically generate coupled algebraic nonlinear systems of equations, which become increasingly more difficult to solve as their sizes increase. This leads to longer computation times and can cause the solver to fail. These problems also arise when using the equation-based modelling language Modelica and Annex 60 based libraries. This may limit the applicability of the library to relatively small problems unless problems are restructured. This paper discusses two algebraic loop types and presents an approach that decouples algebraic loops into smaller parts, or removes them completely. The approach is applied to a case study model where an algebraic loop of 86 iteration variables is decoupled into smaller parts with a maximum of 5 iteration variables.

1 aJorissen, Filip1 aWetter, Michael1 aHelsen, Lieve uhttps://simulationresearch.lbl.gov/publications/simplifications-hydronic-system02483nas a2200241 4500008004100000245008900041210006900130260001200199300001400211490000700225520173000232653002201962653002501984653002402009653001502033653002202048653001802070100001702088700001502105700001902120700001402139856008802153 2017 eng d00aSimulation and visualization of energy-related occupant behavior in office buildings0 aSimulation and visualization of energyrelated occupant behavior  c03/2017 a785–7980 v103 a

In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.

10aBehavior Modeling10abuilding performance10abuilding simulation10aenergyplus10aoccupant behavior10avisualization1 aChen, Yixing1 aLiang, Xin1 aHong, Tianzhen1 aLuo, Xuan uhttps://simulationresearch.lbl.gov/publications/simulation-and-visualization-energy03410nas 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-vs02293nas a2200241 4500008004100000245016600041210006900207520145100276653002201727653001801749653002101767653002301788653002501811653002201836653001501858100001401873700001201887700001701899700001901916700001401935700001501949856008701964 2017 eng d00aSpatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings0 aSpatial Distribution of Internal Heat Gains A Probabilistic Repr3 a

Internal heat gains from occupants, lighting, and plug loads are significant components of the space cooling load in an office building. Internal heat gains vary with time and space. The spatial diversity is significant, even for spaces with the same function in the same building. The stochastic nature of internal heat gains makes determining the peak cooling load to size air-conditioning systems a challenge. The traditional conservative practice of considering the largest internal heat gain among spaces and applying safety factors overestimates the space cooling load, which leads to oversized air-conditioning equipment and chiller plants. In this study, a field investigation of several large office buildings in China led to the development of a new probabilistic approach that represents the spatial diversity of the design internal heat gain of each tenant as a probability distribution function. In a large office building, a central chiller plant serves all air handling units (AHUs), with each AHU serving one or more floors of the building. Therefore, the spatial diversity should be considered differently when the peak cooling loads to size the AHUs and chillers are calculated. The proposed approach considers two different levels of internal heat gains to calculate the peak cooling loads and size the AHUs and chillers in order to avoid oversizing, improve the overall operating efficiency, and thus reduce energy use.

10aair handling unit10achiller plant10aequipment sizing10ainternal heat gain10aspatial distribution10aspatial diversity10astochastic1 aZhang, Qi1 aYan, Da1 aAn, Jingjing1 aHong, Tianzhen1 aTian, Wei1 aSun, Kaiyu uhttps://simulationresearch.lbl.gov/publications/spatial-distribution-internal-heat02072nas 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-social02035nas a2200181 4500008004100000245009300041210006900134520139300203653002101596653002201617653003601639653001901675653001501694653002201709100001501731700001901746856008801765 2016 eng d00aA Simulation Approach to Estimate Energy Savings Potential of Occupant Behavior Measures0 aSimulation Approach to Estimate Energy Savings Potential of Occu3 a

Occupant behavior in buildings is a leading factor influencing energy use in buildings. Low-cost behavioral solutions have demonstrated significant potential energy savings. Estimating the behavioral savings potential is important for a more effective design of behavior change interventions, which in turn will support more effective energy-efficiency policies. This study introduces a simulation approach to estimate the energy savings potential of occupant behavior measures. First it defines five typical occupant behavior measures in office buildings, then simulates and analyzes their individual and integrated impact on energy use in buildings. The energy performance of the five behavior measures was evaluated using EnergyPlus simulation for a real office building across four typical U.S. climates and two vintages. The Occupancy Simulator was used to simulate the occupant movement in each zone with inputs from the site survey of the case building. Based on the simulation results, the occupant behavior measures can achieve overall site energy savings as high as 22.9% for individual measures and up to 41.0% for integrated measures. Although energy savings of behavior measures would vary depending upon many factors, the presented simulation approach is robust and can be adopted for other studies aiming to quantify occupant behavior impact on building performance.

10abehavior measure10aBehavior Modeling10abuilding performance simulation10aenergy savings10aenergyplus10aoccupant behavior1 aSun, Kaiyu1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/simulation-approach-estimate-energy01903nas a2200229 4500008003900000245004100039210004100080260001200121300001200133490000700145520126600152653002401418653001801442653001401460653002201474653002001496653002401516100001901540700001201559700001901571856008301590 2015 d00aSimulation of Occupancy in Buildings0 aSimulation of Occupancy in Buildings c01/2015 a348-3590 v873 a

Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2–4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.

10abuilding simulation10aco-simulation10aoccupancy10aoccupant behavior10asoftware module10astochastic modeling1 aFeng, Xiaohang1 aYan, Da1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/simulation-occupancy-buildings01114nas a2200121 4500008003900000245009700039210006900136520064700205100002000852700002000872700001800892856008200910 2015 d00aSimulation Speed Analysis and Improvements of Modelica Models for Building Energy Simulation0 aSimulation Speed Analysis and Improvements of Modelica Models fo3 a

This paper presents an approach for speeding up Modelica models. Insight is provided into how Modelica models are solved and what determines the tool’s computational speed. Aspects such as algebraic loops, code efficiency and integrator choice are discussed. This is illustrated using simple building simulation examples and Dymola. The generality of the work is in some cases verified using OpenModelica. Using this approach, a medium sized office building including building envelope, heating ventilation and air conditioning (HVAC) systems and control strategy can be simulated at a speed five hundred times faster than real time.

1 aJorissen, Filip1 aWetter, Michael1 aHelsen, Lieve uhttps://simulationresearch.lbl.gov/publications/simulation-speed-analysis-and02296nas a2200193 4500008003900000245011200039210006900151520161300220653002401833653002401857653002201881653002201903653002301925653002401948100001501972700001901987700001502006856008102021 2014 d00aStochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration0 aStochastic Modeling of Overtime Occupancy and Its Application in3 a

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

10abuilding energy use10abuilding simulation10amodel calibration10aoccupant behavior10aovertime occupancy10astochastic modeling1 aSun, Kaiyu1 aHong, Tianzhen1 aGuo, Siyue uhttps://simulationresearch.lbl.gov/publications/stochastic-modeling-overtime02408nas a2200229 4500008003900000022001400039245011700053210006900170260001200239300001200251490000600263520165000269653002401919653002001943653002201963653002201985653002102007653002502028100002002053700001902073856008602092 2013 d a1996-874400aStatistical Analysis and Modeling of Occupancy Patterns in Open-Plan Offices using Measured Lighting-Switch Data0 aStatistical Analysis and Modeling of Occupancy Patterns in OpenP c03/2013 a23–320 v63 a

Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve, a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.

10abuilding simulation10aoccupancy model10aoccupancy pattern10aoccupant schedule10aoffice buildings10astatistical analysis1 aChang, Wen-Kuei1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/statistical-analysis-and-modeling02446nas a2200277 4500008004100000245004600041210004600087260003100133520169300164653001401857653001101871653002201882653001101904653001001915653001501925653002101940653001401961100001801975700001901993700001302012700001502025700001902040700001802059700001802077856007302095 2012 eng d00aSustainable Campus with PEV and Microgrid0 aSustainable Campus with PEV and Microgrid aPacific Grove, CAc08/20193 a

Market penetration of electric vehicles (EVs) is gaining momentum, as is the move
towards increasingly distributed, clean and renewable electricity sources. EV charging shifts a
significant portion of transportation energy use onto building electricity meters. Hence,
integration strategies for energy-efficiency in buildings and transport sectors are of increasing
importance. This paper focuses on a portion of that integration: the analysis of an optimal
interaction of EVs with a building-serving transformer, and coupling it to a microgrid that
includes PV, a fuel cell and a natural gas micro-turbine. The test-case is the Nanyang
Technological University (NTU), Singapore campus. The system under study is the Laboratory
of Clean Energy Research (LaCER) Lab that houses the award winning Microgrid Energy
Management System (MG-EMS) project. The paper analyses three different case scenarios to
estimate the number of EVs that can be supported by the building transformer serving LaCER.
An approximation of the actual load data collected for the building into different time intervals is
performed for a transformer loss of life (LOL) calculation. The additional EV loads that can be
supported by the transformer with and without the microgrid are analyzed. The numbers of
possible EVs that can be charged at any given time under the three scenarios are also determined.
The possibility of using EV fleet at NTU campus to achieve demand response capability and
intermittent PV output leveling through vehicle to grid (V2G) technology and building energy
management systems is also explored.

10abuildings10acampus10aelectric vehicles10aenergy10aloads10amicrogrids10arenewable energy10atransport1 aKumar, Nandha1 aSivaneasan, B.1 aSo, P.L.1 aGooi, H.B.1 aJadhav, Nilesh1 aSingh, Reshma1 aMarnay, Chris uhttps://aceee.org/files/proceedings/2012/data/papers/0193-000363.pdf02272nas 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-and00550nas a2200133 4500008004100000245011300041210006900154260003100223100001800254700001800272700002100290700001800311856008700329 2011 eng d00aSystematic Development of an Operational BIM Utilising Simulation and Performance Data in Building Operation0 aSystematic Development of an Operational BIM Utilising Simulatio aSydney, Australiac11/20111 aCorry, Edward1 aKeane, Marcus1 aO'Donnell, James1 aCosta, Andrea uhttps://simulationresearch.lbl.gov/publications/systematic-development-operational00423nas a2200121 4500008004100000245006300041210006300104300001200167490000700179100001600186700001800202856008100220 2010 eng d00aSimulations of air distribution in buildings by FFD on GPU0 aSimulations of air distribution in buildings by FFD on GPU a783-7960 v161 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/simulations-air-distribution00472nas a2200121 4500008004100000245007400041210006900115100002000184700002500204700001800229700002200247856008100269 2010 eng d00aA Statistical Pattern Analysis Framework for Rooftop Unit Diagnostics0 aStatistical Pattern Analysis Framework for Rooftop Unit Diagnost1 aNajafi, Massieh1 aAuslander, David, M.1 aHaves, Philip1 aSohn, Michael, D. uhttps://simulationresearch.lbl.gov/publications/statistical-pattern-analysis02269nas a2200169 4500008004100000020001800041245011700059210006900176260004100245520162500286100002201911700002201933700001801955700002201973700001801995856008602013 2010 eng d a0-918249-60-000aSystems Approach to Energy Efficient Building Operation: Case Studies and Lessons Learned in a University Campus0 aSystems Approach to Energy Efficient Building Operation Case Stu aAsilomar, California, USAbOmnipress3 a

This paper reviews findings from research conducted at a university campus to develop a robust systems approach to monitor and continually optimize building energy performance. The field analysis, comprising three projects, included detailed monitoring, model-based analysis of system energy performance, and implementation of optimized control strategies for both district and building-scale systems. One project used models of the central cooling plant and campus building loads, and weather forecasts to analyze and optimize the energy performance of a district cooling system, comprising chillers, pumps and a thermal energy storage system. Fullscale implementation of policies devised with a model predictive control approach produced energy savings of about 5%, while demonstrating that the heuristic policies implemented by the operators were close to optimal during peak cooling season and loads. Research was also conducted to evaluate whole building monitoring and control methods. A second project performed in a campus building combined sub-metered end-use data, performance benchmarks, energy simulations and thermal load estimators to create a web-based energy performance visualization tool prototype. This tool provides actionable energy usage information to aid in facility operation and to enable performance improvement. In a third project, an alternative to demand controlled ventilation enabled by direct measurements of building occupancy levels was assessed. Simulations were used to show 5-15% reduction in building HVAC system energy usage when using estimates of actual occupancy levels.

1 aNarayanan, Satish1 aApte, Michael, G.1 aHaves, Philip1 aPiette, Mary, Ann1 aElliott, John uhttps://simulationresearch.lbl.gov/publications/systems-approach-energy-efficient02060nas a2200217 4500008004100000245010500041210006900146260000900215300001200224490000600236520133500242653001601577653002401593653002301617653002201640653003001662653002901692100001501721700001901736856008701755 2009 eng d00aSimulation-based assessment of the energy savings benefits of integrated control in office buildings0 aSimulationbased assessment of the energy savings benefits of int c2009 a239-2510 v23 a

The purpose of this study is to use existing simulation tools to quantify the energy savings benefits of integrated control in office buildings. An EnergyPlus medium office benchmark simulation model (V1.0_3.0) developed by the Department of Energy (DOE) was used as a baseline model for this study. The baseline model was modified to examine the energy savings benefits of three possible control strategies compared to a benchmark case across 16 DOE climate zones. Two controllable subsystems were examined: (1) dimming of electric lighting, and (2) controllable window transmission. Simulation cases were run in EnergyPlus V3.0.0 for building window-to-wall ratios (WWR) of 33% and 66%. All three strategies employed electric lighting dimming resulting in lighting energy savings in building perimeter zones ranging from 64% to 84%. Integrated control of electric lighting and window transmission resulted in heating, ventilation, and air conditioning (HVAC) energy savings ranging from –1% to 40%. Control of electric lighting and window transmission with HVAC integration (seasonal schedule of window transmission control) resulted in HVAC energy savings ranging from 3% to 43%. HVAC energy savings decreased moving from warm climates to cold climates and increased when moving from humid, to dry, to marine climates.

10adaylighting10aenergy conservation10aenergy consumption10aenergy efficiency10aenergy management systems10alighting control systems1 aShen, Eric1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/simulation-based-assessment-energy02217nas a2200217 4500008004100000020002200041245006700063210006400130250000700194260006500201490000700266520151600273653001301789100002101802700002301823700001801846700002001864700002301884700002001907856007201927 2009 eng d a978-91-7393-513-500aStandardization of thermo-fluid modeling in Modelica.Fluid 1.00 aStandardization of thermofluid modeling in ModelicaFluid 10 a13 aComo, ItalybLinköping University Electronic Pressc09/20090 v433 a

This article discusses the Modelica.Fluid library that has been included in the Modelica Standard Library 3.1. Modelica.Fluid provides interfaces and basic components for the device-oriented modeling of one dimensional thermo-fluid flow in networks containing vessels; pipes; fluid machines; valves and fittings.

A unique feature of Modelica.Fluid is that the component equations and the media models as well as pressure loss and heat transfer correlations are decoupled from each other. All components are implemented such that they can be used for media from the Modelica.Media library. This means that an incompressible or compressible medium; a single or a multiple substance medium with one or more phases might be used with one and the same model as long as the modeling assumptions made hold. Furthermore;

trace substances are supported. Modeling assumptions can be configured globally in an outer System object. This covers in particular the initialization; uni- or bi-directional flow; and dynamic or steady-state formulation of mass; energy; and momentum balance. All assumptions can be locally refined for every component.

While Modelica.Fluid contains a reasonable set of component models; the goal of the library is not to provide a comprehensive set of models; but rather to provide interfaces and best practices for the treatment of issues such as connector design and implementation of energy; mass and momentum balances. Applications from various domains are presented.

10amodelica1 aFranke, Rüdiger1 aCasella, Francesco1 aOtter, Martin1 aProelss, Katrin1 aSielemann, Michael1 aWetter, Michael uhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=1300539nas 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-delivery01795nas a2200181 4500008004100000050001500041245009300056210006900149260001200218300001200230490000800242520124800250100001801498700001701516700002001533700001301553856004701566 2007 eng d aLBNL-6097900aA Semi-automated Commissioning Tool for VAV Air Handling Units: Functional Test Analyzer0 aSemiautomated Commissioning Tool for VAV Air Handling Units Func c01/2007 a380-3910 v1133 a

A software tool that automates the analysis of functional tests for air-handling units is described. The tool compares the performance observed during manual tests with the performance predicted by simple models of the components under test that are configured using design information and catalog data. Significant differences between observed and expected performance indicate the presence of faults. Fault diagnosis is performed by analyzing the variation of these differences with operating point using expert rules and fuzzy inferencing.

The tool has a convenient user interface to facilitate manual entry of measurements made during a test. A graphical display compares the measured and expected performance, highlighting significant differences that indicate the presence of faults. The tool is designed to be used by commissioning providers conducting functional tests as part of either new building commissioning or retro-commissioning, as well as by building owners and operators conducting routine tests to check the performance of their HVAC systems. The paper describes the input data requirements of the tool, the software structure, the graphical interface, and summarizes the development and testing process used.

1 aHaves, Philip1 aKim, Moosung1 aNajafi, Massieh1 aXu, Peng uhttp://gaia.lbl.gov/btech/papers/60979.pdf00385nas a2200097 4500008004100000245006100041210005600102260002800158100001500186856008600201 2007 eng d00aA Simplified Hot Water Distribution System Model (DOE-2)0 aSimplified Hot Water Distribution System Model DOE2 aBeijing, Chinac09/20071 aGu, Lixing uhttps://simulationresearch.lbl.gov/publications/simplified-hot-water-distribution00501nas a2200133 4500008004100000245006700041210006700108260002800175100002100203700002400224700002000248700001500268856008400283 2007 eng d00aSimulation Enhanced Prototyping of an Experimental Solar House0 aSimulation Enhanced Prototyping of an Experimental Solar House aBeijing, Chinac09/20071 aChoudhary, Ruchi1 aAugenbroe, Godfried1 aGentry, Russell1 aHu, Huafen uhttps://simulationresearch.lbl.gov/publications/simulation-enhanced-prototyping00465nas a2200121 4500008004100000245005800041210005800099260002800157100002100185700002500206700002300231856008900254 2007 eng d00aSimulation of Energy Management Systems in EnergyPlus0 aSimulation of Energy Management Systems in EnergyPlus aBeijing, Chinac09/20071 aEllis, Peter, G.1 aTorcellini, Paul, A.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/simulation-energy-management-systems00469nas a2200121 4500008004100000245007900041210006900120260002800189100001600217700001600233700001800249856008000267 2007 eng d00aThe Study of a Simple HVAC Interface of EnergyPlus in the Chinese Language0 aStudy of a Simple HVAC Interface of EnergyPlus in the Chinese La aBeijing, Chinac09/20071 aLiu, Junjie1 aLi, Wenshen1 aZhou, Xiaojie uhttps://simulationresearch.lbl.gov/publications/study-simple-hvac-interface00516nas a2200121 4500008004100000245010000041210006900141260003200210100002000242700002400262700002300286856008500309 2006 eng d00aSimulation Strategies for Healthcare Design to Achieve Comfort and Optimize Building Energy Use0 aSimulation Strategies for Healthcare Design to Achieve Comfort a aCambridge, MA, USAc08/20061 aNarayan, Shruti1 aLavedrine, Isabelle1 aMcClintock, Maurya uhttps://simulationresearch.lbl.gov/publications/simulation-strategies-healthcare02109nas a2200157 4500008004100000024001700041050001500058245005600073210005300129260002200182520161100204100001301815700001801828700001701846856008801863 2005 eng d aLBNL/PUB-933 aLBNL-5864800aA Semi-Automated Functional Test Data Analysis Tool0 aSemiAutomated Functional Test Data Analysis Tool aNew York City, NY3 a

The growing interest in commissioning is creating a demand that will increasingly be met by mechanical contractors and less experienced commissioning agents. They will need tools to help them perform commissioning effectively and efficiently. The widespread availability of standardized procedures, accessible in the field, will allow commissioning to be specified with greater certainty as to what will be delivered, enhancing the acceptance and credibility of commissioning. In response, a functional test data analysis tool is being developed to analyze the data collected during functional tests for air-handling units.

The functional test data analysis tool is designed to analyze test data, assess performance of the unit under test and identify the likely causes of the failure. The tool has a convenient user interface to facilitate manual entry of measurements made during a test. A graphical display shows the measured performance versus the expected performance, highlighting significant differences that indicate the unit is not able to pass the test. The tool is described as semi-automated because the measured data need to be entered manually, instead of being passed from the building control system automatically. However, the data analysis and visualization are fully automated. The tool is designed to be used by commissioning providers conducting functional tests as part of either new building commissioning or retro-commissioning, as well as building owners and operators interested in conducting routine tests periodically to check the performance of their HVAC systems.

1 aXu, Peng1 aHaves, Philip1 aKim, Moosung uhttps://simulationresearch.lbl.gov/publications/semi-automated-functional-test-data00405nas a2200109 4500008004100000245004700041210004700088260003000135100002100165700002500186856008400211 2005 eng d00aSimulating Tall Buildings Using EnergyPlus0 aSimulating Tall Buildings Using EnergyPlus aMontreal, Canadac08/20051 aEllis, Peter, G.1 aTorcellini, Paul, A. uhttps://simulationresearch.lbl.gov/publications/simulating-tall-buildings-using01965nas a2200145 4500008004100000050001500041245007800056210006900134260002500203520144600228100001301674700001801687700002401705856009001729 2005 eng d aLBNL-5580100aA Simulation-Based Testing and Training Environment for Building Controls0 aSimulationBased Testing and Training Environment for Building Co aBoulder, COc08/20043 a

A hybrid simulation environment for controls testing and training is described. A real-time simulation of a building and HVAC system is coupled to a real building control system using a hardware interface. A prototype has been constructed and tested in which the dynamic performance of both the HVAC equipment and the building envelope is simulated using SPARK (Simulation Problem Analysis and Research Kernel). A low cost hardware interface between the simulation and the real control system is implemented using plug-in analog-to-digital and digital-to-analog cards in a personal computer. The design and implementation of the hardware interface in SPARK are described. The development of a variant of this environment that uses a derivative of EnergyPlus to test the implementation of a natural ventilation control strategy in real control hardware is also described.

Various applications of the hybrid simulation environment are briefly described, including the development of control algorithms and strategies, control system product testing and the pre-commissioning of building control system installations. The application to the education and training of building operators and HVAC service technicians is discussed in more detail, including the development of a community college curriculum that includes the use of the hybrid simulation environment to teach both control system configuration and HVAC troubleshooting.

1 aXu, Peng1 aHaves, Philip1 aDeringer, Joseph, J uhttps://simulationresearch.lbl.gov/publications/simulation-based-testing-and-training00485nas a2200109 4500008004100000245010800041210006900149260003000218100002200248700001800270856008700288 2005 eng d00aSpecifiction of an IFC-Based Intelligent Graphical User Interface to Support Building Energy Simulation0 aSpecifiction of an IFCBased Intelligent Graphical User Interface aMontreal, Canadac08/20051 aO'Sullivan, Barry1 aKeane, Marcus uhttps://simulationresearch.lbl.gov/publications/specifiction-ifc-based-intelligent00506nas 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-daylighting00423nas a2200109 4500008004100000245005000041210004900091260005800140490001000198100002000208856008500228 2004 eng d00aSimulation-Based Building Energy Optimization0 aSimulationBased Building Energy Optimization aBerkeley, CA, USAbUniversity of California, Berkeley0 vPh.D.1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/simulation-based-building-energy02585nas a2200109 4500008004100000245005000041210004900091520221100140653001702351100002002368856008702388 2004 eng d00aSimulation-based building energy optimization0 aSimulationbased building energy optimization3 a

This dissertation presents computational techniques for simulation-based design optimization of buildings and heating, ventilation, air-conditioning and lighting systems in which the cost function is smooth. In such problems, the evaluation of the cost function involves the numerical solution of systems of differential algebraic equations (DAE). Since the termination criteria of the iterative solvers often depend on the design parameters, a computer code for solving such systems usually defines a numerical approximation to the cost function that is discontinuous in the design parameters. The discontinuities can be large in cost functions that are evaluated by commercial building energy simulation programs, and optimization algorithms that require smoothness frequently fail if used with such programs. Furthermore, controlling the numerical approximation error is often not possible with commercial building energy simulation programs.

In this dissertation, we present BuildOpt, a new detailed thermal building and daylighting simulation program. BuildOpt's simulation models dene a DAE system that is smooth in the state variables, in time and in the design parameters. This allows proving that the DAE system has a unique solution that is smooth in the design parameters, and it is required to compute high precision approximating cost functions that converge to a cost function that is smooth in the design parameters as the DAE solver tolerance is tightened.

For simulation programs that allow such a precision control, we constructed subprocedures for Generalized Pattern Search (GPS) optimization algorithms that adaptively control the precision of the cost function evaluations: coarse precision for the early iterations,with precision progressively increasing as a stationary point is approached. This scheme significantly reduces the computation time, and it allows to prove that the sequence of iterates contains stationary accumulation points. For optimization problems in which commercial building energy simulation programs are used to evaluate the cost function, we compared by numerical experiment several deterministic and probabilistic optimization algorithms.

10adissertation1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/simulation-based-building-energy-001945nas a2200145 4500008004100000024001500041245007800056210006900134260001600203520144200219100001301661700001801674700002401692856008301716 2004 eng d aLBNL-5580100aA simulation-based testing and training environment for building controls0 asimulationbased testing and training environment for building co aBoulder, CO3 a

A hybrid simulation environment for controls testing and training is described. A real-time simulation of a building and HVAC system is coupled to a real building control system using a hardware interface. A prototype has been constructed and tested in which the dynamic performance of both the HVAC equipment and the building envelope is simulated using SPARK (Simulation Problem Analysis and Research Kernel). A low cost hardware interface between the simulation and the real control system is implemented using plug-in analog-to-digital and digital-to-analog cards in a personal computer. The design and implementation of the hardware interface in SPARK are described. The development of a variant of this environment that uses a derivative of EnergyPlus to test the implementation of a natural ventilation control strategy in real control hardware is also described. Various applications of the hybrid simulation environment are briefly described, including the development of control algorithms and strategies, control system product testing and the pre-commissioning of building control system installations. The application to the education and training of building operators and HVAC service technicians is discussed in more detail, including the development of a community college curriculum that includes the use of the hybrid simulation environment to teach both control system configuration and HVAC troubleshooting.

1 aXu, Peng1 aHaves, Philip1 aDeringer, Joseph, J uhttps://simulationresearch.lbl.gov/publications/simulation-based-testing-and-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-100587nas 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-000589nas 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-performance01524nas a2200181 4500008004100000050001500041245009700056210006900153260003600222300001000258490000600268520090100274100001901175700001801194700002201212700001801234856009001252 2001 eng d aLBNL-4828600aStrategies for Coupling Energy Simulation Programs and Computational Fluid Dynamics Programs0 aStrategies for Coupling Energy Simulation Programs and Computati aRio de Janeiro, Brazilc08/2001 a59-660 v13 a

Energy simulation (ES) and computational fluid dynamics (CFD) can play important roles in building design by providing complementary information about the buildings' environmental performance. However, separate applications of ES and CFD are usually unable to give an accurate prediction of building performance due to the assumptions involved in the separate calculations. Integration of ES and CFD eliminates many of these assumptions since the information provided by the models is complementary. Several different approaches to integrating ES and CFD are described. In order to bridge the discontinuities of time-scale, spatial resolution and computing speed between ES and CFD programs, a staged coupling strategy for different problems is proposed. The paper illustrates a typical dynamic coupling process by means of an example implemented using the EnergyPlus and MIT-CFD programs.

1 aZhai, Zhiqiang1 aChen, Qingyan1 aKlems, Joseph, H.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/strategies-coupling-energy-simulation02115nas a2200133 4500008004100000245008800041210006900129490000800198520163400206100001801840700002401858700001901882856008001901 1998 eng d00aA Standard Simulation Testbed for the Evaluation of Control Algorithms & Strategies0 aStandard Simulation Testbed for the Evaluation of Control Algori0 v1043 a

This paper, which reports the results of ASHRAE Research Project 825, describes the development of a set of tools and supporting data to facilitate the evaluation of HVAC control algorithms and strategies using computer simulation. The tools consist of a documented set of component models for use in two component-based HVAC simulation programs. New models have been developed to enable explicit simulation of flow rates and pressure drops in ventilation systems, particularly variable-air-volume (VAV) systems, and detailed simulation of algorithms and strategies used in HVAC control systems. A mixed-use building equipped with a VAV HVAC system has been extensively documented, and a detailed model of the fabric, mechanical equipment, and controls has been produced in order to illustrate the capabilities and use of the tools. Values for the parameters in the component models describing the fabric and mechanical equipment are based on construction drawings, manufacturer's specifications, surveys, and measurements. Detailed models of the strategies for fan control, supply air temperature control, and room temperature control were developed from the controls manufacturer's technical information and the configuration of the actual control system. A simulation model of the whole building was then assembled from the models of the fabric, mechanical equipment, and controls. Results obtained by exercising the test bed in order to demonstrate its use in evaluating the performance of interacting control loops are presented. The paper concludes by discussing possible applications and extensions of the test bed.

1 aHaves, Philip1 aNorford, Leslie, K.1 aDeSimone, Mark uhttps://simulationresearch.lbl.gov/publications/standard-simulation-testbed01011nas a2200145 4500008004100000245005500041210005500096260000900151300001200160490000700172520056700179100001900746700001400765856008600779 1995 eng d00aStochastic weather model for building HVAC systems0 aStochastic weather model for building HVAC systems c1995 a521-5320 v303 a

The weather is a multi-dimensional stochastic process; the traditional typical or standard meteorological year is not enough to describe the random behaviour of weather. The model presented in this paper is based on the vector auto-regressive (VAR) time series method. From the validation results, it can be seen that the stochastic weather model is essential to describe real climate behaviour, and the accuracy obtained is sufficient for the application of the stochastic weather model in the simulation and stochastic analysis of building HVAC systems.

1 aHong, Tianzhen1 aJiang, Yi uhttps://simulationresearch.lbl.gov/publications/stochastic-weather-model-building01511nas a2200145 4500008004100000245005400041210005400095260000900149300001200158490000700170520106600177100001401243700001901257856008901276 1993 eng d00aStochastic analysis of building thermal processes0 aStochastic analysis of building thermal processes c1993 a209-2180 v283 a

A methodology is presented for investigating the uncertainty properties of the building thermal processes caused by the random behaviour of the meteorological processes and the casual gains. A detailed building thermal model is used with a stochastic weather model and a random casual gain model. The probability distribution of the zone temperature of the building is calculated directly from these models. The overheating risk has been analysed as an example. The probability distribution of the periods when the zone temperature is higher than the demand temperature is calculated. The result shows all the possible situations rather than only a sample as would be obtained by running a normal simulation using given weather data. The influence of different building components on the overheating risk has been studied. The result shows that the most likely component for overheating risk in a residential building in Beijing is the window size. The thermal mass of the internal walls and the placing of windows have little effect on overheating risk.

1 aJiang, Yi1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/stochastic-analysis-building-thermal00408nas a2200109 4500008004100000245005700041210005700098260002100155100001400176700001900190856008900209 1993 eng d00aStochastic analysis of overheating risk in buildings0 aStochastic analysis of overheating risk in buildings aLondon, UKc19931 aJiang, Yi1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/stochastic-analysis-overheating-risk00501nas a2200133 4500008004100000245008000041210006900121260001400190100002000204700002300224700001300247700001800260856008900278 1991 eng d00aSelf-tuning Control with Fuzzy Rule-Based Supervision for HVAC Applications0 aSelftuning Control with Fuzzy RuleBased Supervision for HVAC App aSingapore1 aLing, Keck-Voon1 aDexter, Arthur, L.1 aGeng, G.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/self-tuning-control-fuzzy-rule-based00373nas a2200109 4500008004100000245003800041210003800079260002200117100002300139700001800162856008300180 1989 eng d00aSimulation of Local Loop Controls0 aSimulation of Local Loop Controls aVancouver, Canada1 aDexter, Arthur, L.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/simulation-local-loop-controls01083nas a2200133 4500008003900000245007600039210006900115260001800184490000700202520061200209100002000821700002600841856008200867 1988 d00aSaving Electricity in Commercial Buildings with Adjustable-Speed Drives0 aSaving Electricity in Commercial Buildings with AdjustableSpeed  bIEEEc05/19880 v243 a

Fan and chiller energy savings achievable in commercial buildings with adjustable-speed drives are described. The savings are estimated with the aid of parametric simulations from a sophisticated, hourly building energy simulation model. Two prototypes-a single-zone retail store and a multizone medium office building-are simulated for five U.S. locations. The model incorporates part-load performance curves for both inlet vane and adjustable-speed drive controls for fans and centrifugal chillers. The results identify economic conditions that justify the added expense of adjustable-speed drives.

1 aEto, Joseph, H.1 aDe Almeida, Anibal, T uhttps://simulationresearch.lbl.gov/publications/saving-electricity-commercial00416nas a2200097 4500008004100000245007900041210006900120260002700189100001800216856008400234 1983 eng d00aSelection and Sizing of Low Energy Cooling Systems for More Humid Climates0 aSelection and Sizing of Low Energy Cooling Systems for More Humi aCrete, Greecec06/19831 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/selection-and-sizing-low-energy00408nas a2200097 4500008004100000245008400041210006900125260001200194100001800206856008600224 1983 eng d00aSERI-RES: a Thermal Simulation Model for Passive Solar and Low Energy Buildings0 aSERIRES a Thermal Simulation Model for Passive Solar and Low Ene c10/19831 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/seri-res-thermal-simulation-model00439nas a2200097 4500008004100000245010600041210006900147260002300216100001800239856008400257 1981 eng d00aSimulated Performance of a Passively Cooled Residence with a Solar Regenerated Desiccant Dehumidifier0 aSimulated Performance of a Passively Cooled Residence with a Sol aMiami, FLc11/19811 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/simulated-performance-passively