@article {30488, title = {Modeling occupancy distribution in large spaces with multi-feature classification algorithm}, journal = {Building and Environment}, volume = {137}, year = {2018}, abstract = {

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{\textquoteright} 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\%.

}, keywords = {energy efficiency, HVAC loads, multi-feature classification algorithm, occupancy distribution, occupancy-based control}, doi = {10.1016/j.buildenv.2018.04.002}, author = {Wei Wang and Jiayu Chen and Tianzhen Hong} } @article {30031, title = {Modeling of HVAC Operational Faults in Building Performance Simulation}, year = {2017}, abstract = {

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{\textemdash}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.

}, keywords = {energy performance, energyplus, hvac system, Modeling and simulation, Operational fault, Thermal comfort }, author = {Rongpeng Zhang and Tianzhen Hong} } @conference {31830, title = {MPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings}, booktitle = {Proceedings of the 15th IBPSA Conference: Building Simulation 2017}, year = {2017}, month = {08/2017}, address = {San Francisco}, abstract = {

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.

}, keywords = {Model predictive control (MPC)}, url = {http://www.ibpsa.org/proceedings/BS2017/BS2017_351.pdf}, author = {David Blum and Michael Wetter} } @conference {60961, title = {Modeling and Simulation of Operational Faults of HVAC Systems Using Energyplus}, year = {2016}, abstract = {

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.

}, author = {Rongpeng Zhang and Tianzhen Hong} } @article {60352, title = {Modelica Buildings Library}, year = {2015}, author = {Michael Wetter and Wangda Zuo and Thierry Stephane Nouidui and Xiufeng Pang} } @conference {3180, title = {Mapping Hvac Systems for Simulation In EnergyPlus}, booktitle = {SimBuild 2012 IBPSA Conference}, year = {2012}, month = {07/2012}, address = {Madison, WI, USA}, abstract = {

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{\textquoteright}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.

}, author = {Tobias Maile and Mangesh Basarkar and James O{\textquoteright}Donnell and Philip Haves and Kevin Settlemyre} } @article {3414, title = {Monitoring-based HVAC Commissioning of an Existing Office Building for Energy Efficiency}, year = {2012}, month = {10/2012}, abstract = {

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.

}, keywords = {benchmarking, commissioning, energyplus, fault detection and diagnostics, functional testing, trend data}, author = {Shankar Earni and Spencer Woodworth and Xiufeng Pang and Jorge Hernandez-Maldonado and Rongxin Yin and Liping Wang and Steve E. Greenberg and John Fiegel and Alma Rubalcava} } @conference {2672, title = {Modeling and simulation of HVAC faults in EnergyPlus}, booktitle = {Building Simulation 2011}, year = {2011}, month = {11/2011}, address = {Australia}, keywords = {advanced building software: energyplus, building simulation, energyplus, faults, fouling, modeling, sensor offset, simulation research group}, author = {Mangesh Basarkar and Philip Haves and Xiufeng Pang and Liping Wang and Tianzhen Hong} } @article {3179, title = {Modeling and simulation of HVAC Results in EnergyPlus}, year = {2011}, month = {11/2011}, author = {Mangesh Basarkar and Xiufeng Pang and Liping Wang and Tianzhen Hong} } @proceedings {2786, title = {Modeling of Heat Transfer in Rooms in the Modelica "Buildings" Library}, journal = {12th Conference of International Building Performance Ssimulation Association}, year = {2011}, month = {11/2011}, pages = {1096-1103}, address = {Sydney, Australia}, author = {Michael Wetter and Wangda Zuo and Thierry Stephane Nouidui} } @article {2583, title = {Multi-Criteria Optimisation using Past, Real Time and Predictive Performance Benchmarks}, journal = {Simulation Modelling Practice and Theory}, volume = {19}, year = {2011}, month = {04/2011}, pages = {1258-1265}, chapter = {1258}, author = {Torrens, J. Ignacio and Marcus Keane and Andrea Costa and James O{\textquoteright}Donnell} } @proceedings {241, title = {Model Predictive Control of Thermal Energy Storage in Building Cooling Systems}, journal = {American Control Conference}, year = {2010}, month = {06/2010}, address = {Baltimore, Maryland, USA}, abstract = {A 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.}, author = {Yudong Ma and Francesco Borrelli and Brandon Hencey and Brian E. Coffey and Sorin Bengea and Philip Haves} } @article {240, title = {Modeling and Measurement Constraints in Fault Diagnostics for HVAC Systems}, journal = {ASME Journal of Dynamic Systems, Measurement, and Controls}, year = {2010}, abstract = {Many 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.}, author = {Massieh Najafi and David M. Auslander and Peter L. Bartlett and Philip Haves and Michael D. Sohn} } @proceedings {57, title = {Modelica Library for Building Heating, Ventilation and Air-Conditioning Systems}, journal = {Proceedings of the 7th International Modelica Conference}, volume = {43}, year = {2009}, month = {09/2009}, edition = {44}, address = {Como, Italy}, abstract = {

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.

}, doi = {10.3384/ecp0943}, url = {http://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=44}, author = {Michael Wetter} } @conference {59, title = {A Modelica-based model library for building energy and control systems}, booktitle = {Proceedings of the 11th IBPSA Conference}, year = {2009}, month = {07/2009}, pages = {652-659}, address = {Glasgow, Scotland}, abstract = {

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.

}, keywords = {modelica}, url = {http://www.ibpsa.org/proceedings/BS2009/BS09_0652_659.pdf}, author = {Michael Wetter} } @article {69, title = {Modelica-based Modeling and Simulation to Support Research and Development in Building Energy and Control Systems}, journal = {Journal of Building Performance Simulation}, volume = {2}, year = {2009}, pages = {143-161}, abstract = {Traditional 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.}, keywords = {modelica}, doi = {10.1080/19401490902818259}, url = {http://www.informaworld.com/smpp/section?content=a911401852\&fulltext=713240928}, author = {Michael Wetter} } @proceedings {394, title = {{\textquotedblleft}The Monitoring,{\textquotedblright} Panel: Chill-Off}, journal = {Silicon Valley Leadership Group Data Center Energy Efficiency Summit}, year = {2009}, month = {10/2009}, address = {Sunnyvale, CA}, author = {Dean Nelson and Brian Day and Geoffrey C. Bell and Prajesh Bhattacharya and Mike Ryan} } @conference {3383, title = {Multi-criteria optimisation using past, historical, real time and predictive performance benchmarks}, booktitle = {SEEP 2009: 3rd International Conference on Sustainable Energy \& Environmental Protection}, year = {2009}, author = {Torrens, J. Ignacio and Marcus Keane and James O{\textquoteright}Donnell and Andrea Costa} } @conference {3386, title = {A Modelica-Based Model Library for Building Energy and Control Systems}, booktitle = {Building Simulation {\textquoteright}09}, year = {2008}, month = {07/2008}, address = {Glasgow, Scotland}, author = {Michael Wetter} } @conference {61, title = {A Modular Building Controls Virtual Test Bed for the Integration of Heterogeneous Systems}, booktitle = {SimBuild 2008, July 30-August 1}, year = {2008}, month = {08/2008}, address = {Berkeley, CA}, abstract = {

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.

}, author = {Michael Wetter and Philip Haves} } @conference {12052, title = {Measured energy performance of a US-China demonstration energy-efficient commercial building}, booktitle = {2007 ASHRAE Winter Meeting, January 27-31, 2007}, year = {2006}, month = {01/2007}, address = {Dallas, TX}, abstract = {

In July 1998, the U.S. Department of Energy (USDOE) and China{\textquoteright}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{\textquoteright}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.

}, author = {Peng Xu and Yu Joe Huang and Ruidong Jin and Guoxiong Yang} } @proceedings {2807, title = {Methodology for Analyzing the Technical Potential for Energy Performance in the U.S. Commercial Buildings Sector With Detailed Energy Modeling}, journal = {SimBuild 2006}, year = {2006}, month = {08/2006}, address = {Cambridge, MA, USA}, author = {Brent T. Griffith and Drury B. Crawley} } @proceedings {2799, title = {A Model for Naturally Ventilated Cavities on the Exteriors of Opaque Building Thermal Envelopes}, journal = {SimBuild 2006}, year = {2006}, month = {08/2006}, address = {Cambridge, MA, USA}, author = {Brent T. Griffith} } @proceedings {47, title = {Modelica versus TRNSYS {\textemdash} A Comparison Between an Equation-Based and a Procedural Modeling Language for Building Energy Simulation}, journal = {Proc. of the 2nd SimBuild Conference}, year = {2006}, month = {08/2006}, address = {Cambridge, MA, USA}, abstract = {

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{\textquoteright}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{\textquoteright}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.

}, url = {http://www.ibpsa.us/pub/simbuild2006/papers/SB06_001_008.pdf}, author = {Michael Wetter and Christoph Haugstetter} } @conference {230, title = {MOSILAB: Ein Modelica-Simulationswerkzeug zur energetischen Geb{\"a}ude- und Anlagensimulation}, booktitle = {16. Symposium Thermische Solarenergie}, year = {2006}, address = {Bad Staffelstein, Germany}, isbn = {3-934681-45-X}, author = {Christoph Nytsch-Geusen and Andre Nordwig and Mathias Vetter and Christoph Wittwer and Thierry Stephane Nouidui and Peter Schneider} } @proceedings {64, title = {Multizone Airflow Model in Modelica}, journal = {Proc. of the 5th International Modelica Conference}, year = {2006}, month = {09/2006}, pages = {431-440}, address = {Vienna, Austria}, abstract = {

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.

}, keywords = {contaminant transport, multizone airflow}, url = {https://www.modelica.org/events/modelica2006/Proceedings/sessions/Session413.pdf}, author = {Michael Wetter} } @proceedings {56, title = {Multizone Building Model for Thermal Building Simulation in Modelica}, journal = {Proc. of the 5th International Modelica Conference}, year = {2006}, month = {9/2006}, pages = {517-526}, address = {Vienna, Austria}, abstract = {We 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.}, url = {https://www.modelica.org/events/modelica2006/Proceedings/sessions/Session5b4.pdf}, author = {Michael Wetter} } @article {273, title = {Model-Based Automated Functional Testing-Methodology and Application to Air Handling Units}, journal = {ASHRAE Transactions}, volume = {111}, year = {2005}, chapter = {979}, author = {Peng Xu and Philip Haves and Moosung Kim} } @conference {2666, title = {Modeling Ground Source Heat Pump Systems in a Building Energy Simulation Program (EnergyPlus)}, booktitle = {IBPSA Building Simulation 2005}, year = {2005}, month = {08/2005}, address = {Montreal, canada}, author = {Daniel E. Fisher and Simon J. Rees} } @proceedings {232, title = {MOSILAB: Development of a modelica based generic simulation tool supporting modal structural dynamics}, journal = {4th International Modelica Conference}, year = {2005}, pages = {pp.527-534}, address = {Hamburg, Germany}, author = {Christoph Nytsch-Geusen and Thilo Ernst and Peter Schneider and Mathias Vetter and Andreas Holm and Juergen Leopold and Ullrich Doll and Andre Nordwig and Peter Schwarz and Christoph Wittwer and Thierry Stephane Nouidui and Gerhardt Schmidt and Alexander Mattes} } @proceedings {399, title = {Modeling the Behavior of F1-ATPase Biomolecular Motors Using Brownian Dynamics Simulation}, journal = {BioDevice Interface Science and Technology Workshop}, year = {2002}, month = {09/2002}, address = {Scottsdale, AZ}, author = {Prajesh Bhattacharya and Patrick E. Phelan} } @proceedings {309, title = {Model-based Performance Monitoring: Review of Diagnostic Methods and Chiller Case Study}, journal = {ACEEE Summer School}, year = {2000}, month = {08/2000}, address = {Asilomar, California, USA}, abstract = {

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{\texttrademark} 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).

}, author = {Philip Haves and Satkartar T. Khalsa} } @proceedings {323, title = {A Model-Based Approach to the Commissioning of HVAC Systems}, journal = {CLIMA 2000}, year = {1997}, month = {08/1997}, address = {Brussles, Belgium}, author = {Richard A. Buswell and Philip Haves and Tim I. Salsbury} } @proceedings {329, title = {A Model of a Displacement Ventilation/Chilled Ceiling Cooling System Suitable for Annual Energy Simulation}, journal = {Building Simulation {\textquoteright}95}, year = {1995}, month = {08/1995}, address = {Madison, WI}, author = {Simon J. Rees and Philip Haves} } @proceedings {326, title = {Modelling and Simulation of Low Energy Cooling Systems}, journal = {Tsinghua HVAC-95}, year = {1995}, month = {09/1995}, address = {Bejing, China}, author = {Philip Haves and Simon J. Rees and Harrington, L.} } @proceedings {331, title = {Model-Based Approaches to Fault Detection and Diagnosis in Air-Conditioning System}, journal = {System Simulation in Buildings {\textquoteright}94}, year = {1994}, month = {12/1994}, address = {Li{\`e}ge, Belgium}, author = {Mourad Benouarets and Arthur L. Dexter and Richard S. Fargus and Philip Haves and Tim I. Salsbury and Jonathan A. Wright} } @conference {55644, title = {Modeling Cogeneration Systems with DOE-2.1C}, booktitle = {ASHRAE 1988 Winter Meeting}, year = {1988}, month = {01/1988}, publisher = {LBNL}, organization = {LBNL}, address = {Dallas, TX}, author = {Joseph H. Eto and Steven D. Gates} } @proceedings {362, title = {Measurement of Components of Heat Transfer in Passive Cooling Systems}, journal = {1st International Passive \& Hybrid Cooling Conference}, year = {1981}, month = {11/1981}, address = {Miami, FL}, author = {Fred M. Loxsom and Gene Clark and Merino, M. and Philip Haves} }