02433nas a2200193 4500008004100000245009600041210006900137490000800206520175100214653002201965653001501987653004302002653002702045653002802072100001402100700001602114700001902130856009002149 2018 eng d00aModeling occupancy distribution in large spaces with multi-feature classification algorithm0 aModeling occupancy distribution in large spaces with multifeatur0 v1373 a

Occupancy information enables robust and flexible control of heating, ventilation, and air-conditioning (HVAC) systems in buildings. In large spaces, multiple HVAC terminals are typically installed to provide cooperative services for different thermal zones, and the occupancy information determines the cooperation among terminals. However, a person count at room-level does not adequately optimize HVAC system operation due to the movement of occupants within the room that creates uneven load distribution. Without accurate knowledge of the occupants' spatial distribution, the uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolution occupancy distribution is often perceived as a bottleneck for future improvements to HVAC operation efficiency. To fill this gap, this study proposes a multi-feature k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable, low-cost Bluetooth Low Energy (BLE) networks. An on-site experiment was conducted in a typical office of an institutional building to demonstrate the proposed methods, and the experiment outcomes of three case studies were examined to validate detection accuracy. One method based on City Block Distance (CBD) was used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. The results show the accuracy when CBD = 1 is over 71.4% and the accuracy when CBD = 2 can reach up to 92.9%.

10aenergy efficiency10aHVAC loads10amulti-feature classification algorithm10aoccupancy distribution10aoccupancy-based control1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-occupancy-distribution-large02386nas a2200181 4500008004100000245007500041210006900116520176700185653002301952653001501975653001601990653002802006653002202034653002402056100002002080700001902100856008502119 2017 eng d00aModeling of HVAC Operational Faults in Building Performance Simulation0 aModeling of HVAC Operational Faults in Building Performance Simu3 a

Operational faults are common in the heating, ventilating, and air conditioning (HVAC) systems of existing buildings, leading to a decrease in energy efficiency and occupant comfort. Various fault detection and diagnostic methods have been developed to identify and analyze HVAC operational faults at the component or subsystem level. However, current methods lack a holistic approach to predicting the overall impacts of faults at the building level—an approach that adequately addresses the coupling between various operational components, the synchronized effect between simultaneous faults, and the dynamic nature of fault severity. This study introduces the novel development of a fault-modeling feature in EnergyPlus which fills in the knowledge gap left by previous studies. This paper presents the design and implementation of the new feature in EnergyPlus and discusses in detail the fault-modeling challenges faced. The new fault-modeling feature enables EnergyPlus to quantify the impacts of faults on building energy use and occupant comfort, thus supporting the decision making of timely fault corrections. Including actual building operational faults in energy models also improves the accuracy of the baseline model, which is critical in the measurement and verification of retrofit or commissioning projects. As an example, EnergyPlus version 8.6 was used to investigate the impacts of a number of typical operational faults in an office building across several U.S. climate zones. The results demonstrate that the faults have significant impacts on building energy performance as well as on occupant thermal comfort. Finally, the paper introduces future development plans for EnergyPlus fault-modeling capability.

10aenergy performance10aenergyplus10ahvac system10aModeling and simulation10aOperational fault10aThermal comfort  1 aZhang, Rongpeng1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-hvac-operational-faults01392nas 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.pdf01991nas a2200109 4500008003900000245008300039210006900122520156300191100002001754700001901774856008801793 2016 d00aModeling and Simulation of Operational Faults of HVAC Systems Using Energyplus0 aModeling and Simulation of Operational Faults of HVAC Systems Us3 a

HVAC operations play a significant role among various driving factors to improve energy performance of buildings. Extensive researches have been conducted on the design efficiencies and control strategies of HVAC system, but very few focused on the impacts of its operational faults on the building energy efficiency. Modeling and simulation of operational faults can lead to better understandings of the fault impacts and thus support decision making of timely fault corrections which can further benefit the efficient system operation, improve the indoor thermal comfort, and prolong the equipment service life. Fault modeling is also critical to achieve more accurate and reliable model calibrations. This paper introduces the modeling and simulation of operational faults using EnergyPlus, a comprehensive whole building performance simulation tool. The paper discusses the challenges of operational fault modeling, and compares three approaches to simulate operational faults using EnergyPlus. The paper also introduces the latest development of native fault objects within EnergyPlus. As an example, EnergyPlus version 8.4 is used to investigate the impacts of the integrated thermostat and humidistat faults in a typical office building across several U.S. climate zones. The results demonstrate that the faults create significant impacts on the building energy performance as well as occupant thermal comfort. At last, the paper introduces the future development plan of EnergyPlus for the further improvement of its fault modeling capability.

1 aZhang, Rongpeng1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-and-simulation-operational00387nas a2200121 4500008003900000245003100039210003100070100002000101700001600121700003100137700001800168856007900186 2015 d00aModelica Buildings Library0 aModelica Buildings Library1 aWetter, Michael1 aZuo, Wangda1 aNouidui, Thierry, Stephane1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/modelica-buildings-library01650nas 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-simulation02060nas a2200277 4500008003900000245009300039210006900132260001200201520117000213653001701383653001801400653001501418653003601433653002301469653001501492100001901507700002301526700001801549700003101567700001701598700001701615700002501632700001701657700002001674856008801694 2012 d00aMonitoring-based HVAC Commissioning of an Existing Office Building for Energy Efficiency0 aMonitoringbased HVAC Commissioning of an Existing Office Buildin c10/20123 a

The performance of Heating, Ventilation and Air Conditioning (HVAC) systems may fail to satisfy design expectations due to improper equipment installation, equipment degradation, sensor failures, or incorrect control sequences. Commissioning identifies and implements cost-effective operational and maintenance measures in buildings to bring them up to the design intent or optimum operation. An existing office building is used as a case study to demonstrate the process of commissioning. Building energy benchmarking tools are applied to evaluate the energy performance for screening opportunities at the whole building level. A large natural gas saving potential was indicated by the building benchmarking results. Faulty operations in the HVAC systems, such as improper operations of air-side economizers, simultaneous heating and cooling, and ineffective optimal start, were identified through trend data analyses and functional testing. The energy saving potential for each commissioning measure is quantified with a calibrated building simulation model. An actual energy saving of 10% was realized after the implementations of cost-effective measures.

10abenchmarking10acommissioning10aenergyplus10afault detection and diagnostics10afunctional testing10atrend data1 aEarni, Shankar1 aWoodworth, Spencer1 aPang, Xiufeng1 aHernandez-Maldonado, Jorge1 aYin, Rongxin1 aWang, Liping1 aGreenberg, Steve, E.1 aFiegel, John1 aRubalcava, Alma uhttps://simulationresearch.lbl.gov/publications/monitoring-based-hvac-commissioning00768nas 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-results00521nas a2200133 4500008004100000245007500041210006900116260003100185300001400216100002000230700001600250700003100266856009000297 2011 eng d00aModeling of Heat Transfer in Rooms in the Modelica "Buildings" Library0 aModeling of Heat Transfer in Rooms in the Modelica Buildings Lib aSydney, Australiac11/2011 a1096-11031 aWetter, Michael1 aZuo, Wangda1 aNouidui, Thierry, Stephane uhttps://simulationresearch.lbl.gov/publications/modeling-heat-transfer-rooms-modelica00559nas a2200157 4500008004100000245009200041210006900133260001200202300001400214490000700228100002100235700001800256700001800274700002100292856008800313 2011 eng d00aMulti-Criteria Optimisation using Past, Real Time and Predictive Performance Benchmarks0 aMultiCriteria Optimisation using Past Real Time and Predictive P c04/2011 a1258-12650 v191 aTorrens, Ignacio1 aKeane, Marcus1 aCosta, Andrea1 aO'Donnell, James uhttps://simulationresearch.lbl.gov/publications/multi-criteria-optimisation-using-001213nas 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-constraints01678nas a2200133 4500008004100000245008400041210006900125250000700194260002500201490000700226520121900233100002001452856007201472 2009 eng d00aModelica Library for Building Heating, Ventilation and Air-Conditioning Systems0 aModelica Library for Building Heating Ventilation and AirConditi a44 aComo, Italyc09/20090 v433 a

The Buildings library is a freely available Modelica library that is based on Modelica.Fluid. It contains component models for building heating, ventilation and air conditioning systems. It also contains an interface that allows co-simulation with the Ptolemy software framework for concurrent, real-time, embedded systems developed by the University of California at Berkeley. The primary applications are controls design, energy analysis and model-based operation. The library has been used to model hydronic space heating systems, variable air volume flow systems and it has been linked to the EnergyPlus building energy simulation program for co-simulation using Ptolemy II. The library contains dynamic and steady-state component models that are applicable for analyzing fast transients when designing control algorithms and for conducting annual simulations when assessing energy performance. For most models, dimensional analysis is used to compute the performance for operating points that differ from nominal conditions. This allows parameterizing models in the absence of detailed geometrical information which is often impractical to obtain during the conceptual design phase of building systems.

1 aWetter, Michael uhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=4401978nas a2200133 4500008004100000245007500041210006900116260003100185300001200216520152100228653001301749100002001762856006201782 2009 eng d00aA Modelica-based model library for building energy and control systems0 aModelicabased model library for building energy and control syst aGlasgow, Scotlandc07/2009 a652-6593 a

This paper describes an open-source library with component models for building energy and control systems that is based on Modelica, an equation-based object oriented language that is well positioned to become the standard for modeling of dynamic systems in various industrial sectors. The library is currently developed to support computational science and engineering for innovative building energy and control systems. Early applications will include controls design and analysis, rapid prototyping to support innovation of new building systems and the use of models during operation for controls, fault detection and diagnostics. This paper discusses the motivation for selecting an equation-based object-oriented language. It presents the architecture of the library and explains how base model scan be used to rapidly implement new models. To demonstrate the capability of analyzing novel energy and control systems, the paper closes with an example where we compare the dynamic performance of a conventional hydronic heating system with thermostatic radiator valves to an innovative heating system. In the new system, instead of a centralized circulation pump, each of the 18 radiators has a pump whose speed is controlled using a room temperature feedback loop, and the temperature of the boiler is controlled based on the speed of the radiator pump. All flows are computed by solving for the pressure distribution in the piping network, and the controls include continuous and discrete time controls.

10amodelica1 aWetter, Michael uhttp://www.ibpsa.org/proceedings/BS2009/BS09_0652_659.pdf01955nas a2200133 4500008004100000245011800041210006900159300001200228490000600240520145900246653001301705100002001718856008301738 2009 eng d00aModelica-based Modeling and Simulation to Support Research and Development in Building Energy and Control Systems0 aModelicabased Modeling and Simulation to Support Research and De a143-1610 v23 aTraditional building simulation programs possess attributes that make them difficult to use for the design and analysis of building energy and control systems and for the support of model-based research and development of systems that may not already be implemented in these programs. This paper presents characteristic features of such applications, and it shows how equation-based object-oriented modeling can meet requirements that arise in such applications. Next, the implementation of an open-source component model library for building energy systems is presented. The library has been developed using the equation-based object-oriented Modelica modeling language. Technical challenges of modeling and simulating such systems are discussed. Research needs are presented to make this technology accessible to user groups that have more stringent requirements with respect to the numerical robustness of simulation than a research community may have. Two examples are presented in which models from the here described library were used. The first example describes the design of a controller for a nonlinear model of a heating coil using model reduction and frequency domain analysis. The second example describes the tuning of control parameters for a static pressure reset controller of a variable air volume flow system. The tuning has been done by solving a non-convex optimization problem that minimizes fan energy subject to state constraints.10amodelica1 aWetter, Michael uhttp://www.informaworld.com/smpp/section?content=a911401852&fulltext=71324092800477nas 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-chill00500nas a2200121 4500008004100000245010400041210006900145100002100214700001800235700002100253700001800274856008600292 2009 eng d00aMulti-criteria optimisation using past, historical, real time and predictive performance benchmarks0 aMulticriteria optimisation using past historical real time and p1 aTorrens, Ignacio1 aKeane, Marcus1 aO'Donnell, James1 aCosta, Andrea uhttps://simulationresearch.lbl.gov/publications/multi-criteria-optimisation-using00424nas a2200097 4500008004100000245007500041210006900116260003100185100002000216856009000236 2008 eng d00aA Modelica-Based Model Library for Building Energy and Control Systems0 aModelicaBased Model Library for Building Energy and Control Syst aGlasgow, Scotlandc07/20081 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/modelica-based-model-library-building01943nas a2200121 4500008004100000245009400041210006900135260002600204520146700230100002001697700001801717856008601735 2008 eng d00aA Modular Building Controls Virtual Test Bed for the Integration of Heterogeneous Systems0 aModular Building Controls Virtual Test Bed for the Integration o aBerkeley, CAc08/20083 a

This paper describes the Building Controls Virtual Test Bed (BCVTB) that is currently under development at Lawrence Berkeley National Laboratory. An earlier prototype linked EnergyPlus with controls hardware through embedded SPARK models and demonstrated its value in more cost-effective envelope design and improved controls sequences for the San Francisco Federal Building. The BCVTB presented here is a more modular design based on a middleware that we built using Ptolemy II, a modular software environment for design and analysis of heterogeneous systems. Ptolemy II provides a graphical model building environment, synchronizes the exchanged data and visualizes the system evolution during run-time. Our additions to Ptolemy II allow users to couple to Ptolemy II a prototype version of EnergyPlus, MATLAB/Simulink or other simulation programs for data exchange during run-time. In future work we will also implement a BACnet interface that allows coupling BACnet compliant building automation systems to Ptolemy II. We will present the architecture of the BCVTB and explain how users can add their own simulation programs to the BCVTB. We will then present an example application in which the building envelope and the HVAC system was simulated in EnergyPlus, the supervisory control logic was simulated in MATLAB/Simulink and Ptolemy II was used to exchange data during run-time and to provide real-time visualization as the simulation progresses.

1 aWetter, Michael1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/modular-building-controls-virtual02284nas a2200145 4500008004100000245009700041210006900138260002400207520175000231100001301981700001901994700001702013700001902030856008902049 2006 eng d00aMeasured energy performance of a US-China demonstration energy-efficient commercial building0 aMeasured energy performance of a USChina demonstration energyeff aDallas, TXc01/20073 a

In July 1998, the U.S. Department of Energy (USDOE) and China's Ministry of Science of Technology (MOST) signed a Statement of Work (SOW) to collaborate on the design and construction of an energyefficient demonstration office building and design center to be located in Beijing. The proposed 13,000 m2 (140,000 ft2) nine-story office building would use U.S. energy-efficient materials, space-conditioning systems, controls, and design principles that were judged to be widely replicable throughout China. The SOW stated that China would contribute the land and provide for the costs of the base building, while the U.S. would be responsible for the additional (or marginal) costs associated with the package of energy efficiency andrenewable energy improvements to the building. The project was finished and the building occupied in 2004.

Using DOE-2 to analyze the energy performance of the as-built building, the building obtained 44 out of 69 possible points according to the Leadership in Energy and Environmental Design (LEED) rating, including the full maximum of 10 points in the energy performance section. The building achieved a LEED Gold rating, the first such LEED-rated office building in China, and is 60% more efficient than ASHRAE 90.1-1999. The utility data from the first year's operation match well the analysis results, providing that adjustments are made for unexpected changes in occupancy and operations. Compared with similarly equipped office buildings in Beijing, this demonstration building uses 60% less energy per floor area. However, compared to conventional office buildings with less equipment and window air-conditioners, the building uses slightly more energy per floor area.

1 aXu, Peng1 aHuang, Yu, Joe1 aJin, Ruidong1 aYang, Guoxiong uhttps://simulationresearch.lbl.gov/publications/measured-energy-performance-us-china00530nas a2200109 4500008004100000245014700041210006900188260003200257100002400289700002300313856008400336 2006 eng d00aMethodology for Analyzing the Technical Potential for Energy Performance in the U.S. Commercial Buildings Sector With Detailed Energy Modeling0 aMethodology for Analyzing the Technical Potential for Energy Per aCambridge, MA, USAc08/20061 aGriffith, Brent, T.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/methodology-analyzing-technical00452nas a2200097 4500008004100000245010000041210006900141260003200210100002400242856008800266 2006 eng d00aA Model for Naturally Ventilated Cavities on the Exteriors of Opaque Building Thermal Envelopes0 aModel for Naturally Ventilated Cavities on the Exteriors of Opaq aCambridge, MA, USAc08/20061 aGriffith, Brent, T. uhttps://simulationresearch.lbl.gov/publications/model-naturally-ventilated-cavities01672nas a2200121 4500008004100000245013600041210006900177260003200246520116000278100002001438700002701458856006501485 2006 eng d00aModelica versus TRNSYS — A Comparison Between an Equation-Based and a Procedural Modeling Language for Building Energy Simulation0 aModelica versus TRNSYS A Comparison Between an EquationBased and aCambridge, MA, USAc08/20063 a

The EnergyPlus building energy simulation software has been tested using the IEA HVAC BESTEST E300-E545 series of tests and the IEA HVAC BESTEST Fuel-Fired test series. The first is a series of comparative tests for a single-zone DX cooling system which tests a program's ability to model hourly loads over an expanded range of performance conditions for various air mixing, infiltration, thermostat set-up, overload conditions, and various economizer control schemes. The second is a series of analytical/semianalytical comparative tests for a single-zone fuel-fired furnace which tests a program's ability to model steady state performance, varying outdoor and indoor conditions, and circulating and draft fan operation. Each of these HVAC BESTEST series were used to test EnergyPlus prior to new public releases. The application of these tests proved to be very useful in several ways: a) revealed algorithmic errors which were fixed, b) revealed algorithmic shortcomings which were improved or eliminated through the use of more rigorous calculations for certain components, and c) caught newly introduced bugs before public release of updates.

1 aWetter, Michael1 aHaugstetter, Christoph uhttp://www.ibpsa.us/pub/simbuild2006/papers/SB06_001_008.pdf00641nas a2200169 4500008004100000020001800041245009600059210007000155260003000225100002900255700001900284700002000303700002300323700003100346700002100377856007300398 2006 eng d a3-934681-45-X00aMOSILAB: Ein Modelica-Simulationswerkzeug zur energetischen Gebäude- und Anlagensimulation0 aMOSILAB Ein ModelicaSimulationswerkzeug zur energetischen Gebäud aBad Staffelstein, Germany1 aNytsch-Geusen, Christoph1 aNordwig, Andre1 aVetter, Mathias1 aWittwer, Christoph1 aNouidui, Thierry, Stephane1 aSchneider, Peter uhttps://simulationresearch.lbl.gov/publications/mosilab-ein-modelica01493nas a2200145 4500008004100000245004000041210004000081260002900121300001200150520103200162653002601194653002201220100002001242856008501262 2006 eng d00aMultizone Airflow Model in Modelica0 aMultizone Airflow Model in Modelica aVienna, Austriac09/2006 a431-4403 a

We present the implementation of a library of multi-zone airflow models in Modelica and a comparative model validation with CONTAM. Our models have a similar level of detail as the models in CONTAM and COMIS. The multizone airflow models allow modeling the flow between rooms through doors, staircases or construction cracks. The flow can be caused by buoyancy effects, such as stack effects in high rise buildings or air temperature imbalance between adjoining rooms, by flow imbalance of a ventilation system, or by wind pressure on the building envelope. The here presented library can be used with a Modelica library for thermal building and HVAC system simulation to compute interzonal air flow rates. The combined use facilitates the integrated design of building systems, which is typically required for analyzing the interaction of room control loops in variable air volume flow systems through open doors, the flow in naturally ventilated buildings and the pressure in elevator shafts caused by stacked effects.

10acontaminant transport10amultizone airflow1 aWetter, Michael uhttps://www.modelica.org/events/modelica2006/Proceedings/sessions/Session413.pdf01390nas a2200121 4500008004100000245007300041210006900114260002800183300001200211520094000223100002001163856008501183 2006 eng d00aMultizone Building Model for Thermal Building Simulation in Modelica0 aMultizone Building Model for Thermal Building Simulation in Mode aVienna, Austriac9/2006 a517-5263 aWe present a room model for thermal building simu- lation that we implemented in Modelica. The room model can be used for controls analysis and energy analysis of one or several rooms that are connected through airflow or heat conduction. The room model can assess energy storage in the air and in the build- ing construction materials, heat transfer between the room and the outside environment and the humidity and CO2 release to the room air. The humidity storage in the building construction materials is not modeled. We also describe a novel separation of heat transfer mechanisms on which our room model is built on. The separation allowed a significant reduction in model de- velopment time, and it allows using state-of-the-art programs for computing prior to the thermal building simulation certain energy flows, such as solar heat gain of an active facade without breaking feedback loops between the HVAC system and the room.1 aWetter, Michael uhttps://www.modelica.org/events/modelica2006/Proceedings/sessions/Session5b4.pdf00495nas a2200133 4500008004100000024001500041245009500056210006900151490000800220100001300228700001800241700001700259856008500276 2005 eng d aLBNL-5580200aModel-Based Automated Functional Testing-Methodology and Application to Air Handling Units0 aModelBased Automated Functional TestingMethodology and Applicati0 v1111 aXu, Peng1 aHaves, Philip1 aKim, Moosung uhttps://simulationresearch.lbl.gov/publications/model-based-automated-functional00476nas a2200109 4500008004100000245009800041210006900139260003000208100002300238700002000261856008500281 2005 eng d00aModeling Ground Source Heat Pump Systems in a Building Energy Simulation Program (EnergyPlus)0 aModeling Ground Source Heat Pump Systems in a Building Energy Si aMontreal, canadac08/20051 aFisher, Daniel, E.1 aRees, Simon, J. uhttps://simulationresearch.lbl.gov/publications/modeling-ground-source-heat-pump00873nas a2200253 4500008004100000245010600041210006900147260002100216300001500237100002900252700001700281700002100298700002000319700001800339700002100357700001800378700001900396700001900415700002300434700003100457700002200488700002200510856008700532 2005 eng d00aMOSILAB: Development of a modelica based generic simulation tool supporting modal structural dynamics0 aMOSILAB Development of a modelica based generic simulation tool  aHamburg, Germany app.527-5341 aNytsch-Geusen, Christoph1 aErnst, Thilo1 aSchneider, Peter1 aVetter, Mathias1 aHolm, Andreas1 aLeopold, Juergen1 aDoll, Ullrich1 aNordwig, Andre1 aSchwarz, Peter1 aWittwer, Christoph1 aNouidui, Thierry, Stephane1 aSchmidt, Gerhardt1 aMattes, Alexander uhttps://simulationresearch.lbl.gov/publications/mosilab-development-modelica-based00472nas 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-atpase01872nas a2200133 4500008004100000024001500041245009200056210006900148260003900217520135100256100001801607700002601625856008701651 2000 eng d aLBNL-4594900aModel-based Performance Monitoring: Review of Diagnostic Methods and Chiller Case Study0 aModelbased Performance Monitoring Review of Diagnostic Methods a aAsilomar, California, USAc08/20003 a

The paper commences by reviewing the variety of technical approaches to the problem of detecting and diagnosing faulty operation in order to improve the actual performance of buildings. The review covers manual and automated methods, active testing and passive monitoring, the different classes of models used in fault detection, and methods of diagnosis. The process of model-based fault detection is then illustrated by describing the use of relatively simple empirical models of chiller energy performance to monitor equipment degradation and control problems. The CoolTools™ chiller model identification package is used to fit the DOE-2 chiller model to on-site measurements from a building instrumented with high quality sensors. The need for simple algorithms to reject transient data, detect power surges and identify control problems is discussed, as is the use of energy balance checks to detect sensor problems. The accuracy with which the chiller model can be expected to predict performance is assessed from the goodness of fit obtained and the implications for fault detection sensitivity and sensor accuracy requirements are discussed. A case study is described in which the model was applied retroactively to high-quality data collected in a San Francisco office building as part of a related project (Piette et al. 1999).

1 aHaves, Philip1 aKhalsa, Satkartar, T. uhttps://simulationresearch.lbl.gov/publications/model-based-performance-monitoring00471nas 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-commissioning00484nas a2200109 4500008004100000245011100041210006900152260002500221100002000246700001800266856009000284 1995 eng d00aA Model of a Displacement Ventilation/Chilled Ceiling Cooling System Suitable for Annual Energy Simulation0 aModel of a Displacement VentilationChilled Ceiling Cooling Syste aMadison, WIc08/19951 aRees, Simon, J.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/model-displacement-ventilationchilled00453nas a2200121 4500008004100000245005900041210005900100260002700159100001800186700002000204700001900224856008800243 1995 eng d00aModelling and Simulation of Low Energy Cooling Systems0 aModelling and Simulation of Low Energy Cooling Systems aBejing, Chinac09/19951 aHaves, Philip1 aRees, Simon, J.1 aHarrington, L. uhttps://simulationresearch.lbl.gov/publications/modelling-and-simulation-low-energy00600nas 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-fault00432nas a2200121 4500008003900000024001500039245004800054210004600102260003000148100002000178700002200198856009000220 1988 d aDA-88-21-100aModeling Cogeneration Systems with DOE-2.1C0 aModeling Cogeneration Systems with DOE21C aDallas, TXbLBNLc01/19881 aEto, Joseph, H.1 aGates, Steven, D. uhttps://simulationresearch.lbl.gov/publications/modeling-cogeneration-systems-doe-21c00500nas a2200133 4500008004100000245007400041210006900115260002300184100002100207700001600228700001500244700001800259856008900277 1981 eng d00aMeasurement of Components of Heat Transfer in Passive Cooling Systems0 aMeasurement of Components of Heat Transfer in Passive Cooling Sy aMiami, FLc11/19811 aLoxsom, Fred, M.1 aClark, Gene1 aMerino, M.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/measurement-components-heat-transfer