@article {32192, title = {Culture, conformity, and carbon? A multi-country analysis of heating and cooling practices in office buildings}, journal = {Energy Research \& Social Science}, volume = {61}, year = {2020}, month = {Jan-03-2020}, pages = {101344}, abstract = {

This study investigates human-building interaction in office spaces across multiple countries including Brazil, Italy, Poland, Switzerland, the United States, and Taiwan. We analyze social-psychological, contextual, and demographic factors to explain cross-country differences in adaptive thermal actions (i.e. cooling and heating behaviors) and conformity to the norms of sharing indoor environmental control features, an indicator of energy consumption. Specifically, personal adjustments such as putting on extra clothes are generally preferred over technological solutions such as adjusting thermostats in reaction to thermal discomfort. Social-psychological factors including attitudes, perceived behavioral control, injunctive norms, and perceived impact of indoor environmental quality on work productivity influence occupants{\textquoteright} intention to conform to the norms of sharing environmental control features. Lastly, accessibility to environmental control features, office type, gender, and age are also important factors. These findings demonstrate the roles of social-psychological and certain contextual factors in occupants{\textquoteright}
interactions with building design as well as their behavior of sharing environmental control features, both of which significantly influence building energy consumption, and thus, broader decarbonization.

}, issn = {22146296}, doi = {10.1016/j.erss.2019.101344}, author = {Chen, Chien-fei and Tianzhen Hong and de Rubens, Gerardo Zarazua and Yilmaz, Selin and Bandurski, Karol and B{\'e}lafi, Zs{\'o}fia Deme and De Simone, Marilena and Bavaresco, Mateus Vin{\'\i}cius and Wang, Yu and Liu, Pei-ling and Barthelmes, Verena M. and Adams, Jacqueline and D{\textquoteright}Oca, Simona and Przybylski, {\L}ukasz} } @article {31362, title = {Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems}, journal = {Applied Energy}, volume = {236}, year = {2019}, month = {02/2019}, pages = {410 - 425}, abstract = {

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

}, keywords = {building simulation, hvac, Model predictive control, System identification}, issn = {03062619}, doi = {10.1016/j.apenergy.2018.11.093}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261918318099https://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/plain}, author = {David Blum and K. Arendt and Lisa Rivalin and Mary Ann Piette and Michael Wetter and C.T. Veje} } @conference {31980, title = {Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings}, booktitle = {IBPSA Building Simulation 2019}, year = {2019}, address = {Rome, Italy}, abstract = {

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

}, keywords = {benchmarking, building simulation, Model predictive control, software development}, author = {David Blum and Filip Jorissen and Sen Huang and Yan Chen and Javier Arroyo and Kyle Benne and Yanfei Li and Valentin Gavan and Lisa Rivalin and Lieve Helsen and Draguna Vrabie and Michael Wetter and Marina Sofos} } @article {31306, title = {Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings}, journal = {Energy and Buildings}, volume = {174}, year = {2018}, month = {09/2018}, pages = {214 - 227}, abstract = {

Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.

}, keywords = {AC usage benchmarking, Air-conditioning, Clustering analysis, KPIs, residential building, Use pattern}, issn = {03787788}, doi = {10.1016/j.enbuild.2018.06.035}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818307199https://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/plain}, author = {Jingjing An and Da Yan and Tianzhen Hong} } @article {30490, title = {Comparative Study of Air-Conditioning Energy Use of Four Office Buildings in China and USA}, journal = {Energy and Buildings}, volume = {169}, year = {2018}, abstract = {

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. This study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. Overall, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.

}, keywords = {Building envelope, climate, energy consumption, occupant behavior, office buildings, technological choice}, doi = {10.1016/j.enbuild.2018.03.073}, author = {Xin Zhou and Da Yan and Jingjing An and Tianzhen Hong and Xing Shi and Xing Jin} } @article {31305, title = {Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California}, journal = {Applied Energy}, volume = {225}, year = {2018}, month = {09/2018}, pages = {522 - 534}, abstract = {

Climate change and increased electrification of space and water heating in buildings can significantly affect future electricity demand and hourly demand profiles, which has implications for electric grid greenhouse gas emissions and capacity requirements. We use EnergyPlus to quantify building energy demand under historical and under several climate change projections of 32 kinds of building prototypes in 16 different climate zones of California and imposed these impacts on a year 2050 electric grid configuration by simulation in the Holistic Grid Resource Integration and Deployment (HIGRID) model. We find that climate change only prompted modest increases in grid resource capacity and negligible difference in greenhouse gas emissions since the additional electric load generally occurred during times with available renewable generation. Heating electrification, however, prompted a 30{\textendash}40\% reduction in greenhouse gas emissions but required significant grid resource capacity increases, due to the higher magnitude of load increases and lack of readily available renewable generation during the times when electrified heating loads occurred. Overall, this study translates climate change and electrification impacts to system-wide endpoint impacts on future electric grid configurations and highlights the complexities associated with translating building-level impacts to electric system-wide impacts.

}, keywords = {Building Energy Demand, Climate Change Impacts, electric grid, Heating Electrification Effects}, issn = {03062619}, doi = {10.1016/j.apenergy.2018.05.003}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261918306962https://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/plain}, author = {Brian Tarroja and Felicia Chiang and Amir AghaKouchak and Scott Samuelsen and Shuba V. Raghavan and Max Wei and Kaiyu Sun and Tianzhen Hong} } @article {62077, title = {Energy saving potential of a two-pipe system for simultaneous heating and cooling of office buildings}, journal = {Energy and Buildings}, volume = {134}, year = {2017}, month = {01/2017}, pages = {234 - 247}, abstract = {

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

}, keywords = {active beams, energy saving, HVAC systems, low-exergy, modelica, simulation}, issn = {03787788}, doi = {10.1016/j.enbuild.2016.10.051}, author = {Alessandro Maccarini and Michael Wetter and Alireza Afshari and Goran Hultmark and Niels Bergsoe and Anders Vorre} } @article {29897, title = {A Novel Stochastic Modeling Method to Simulate Cooling Loads in Residential Districts}, year = {2017}, abstract = {

District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized;this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2)occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution,and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

}, author = {Jingjing An and Da Yan and Tianzhen Hong and Kaiyu Sun} } @article {30312, title = {Spatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings}, journal = {Energy and Buildings}, year = {2017}, abstract = {

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.

}, keywords = {air handling unit, chiller plant, equipment sizing, internal heat gain, spatial distribution, spatial diversity, stochastic}, doi = {10.1016/j.enbuild.2017.01.044}, author = {Qi Zhang and Da Yan and Jingjing An and Tianzhen Hong and Wei Tian and Kaiyu Sun} } @article {30303, title = {A Thorough Assessment of China{\textquoteright}s Standard for Energy Consumption of Buildings}, journal = {Energy and Buildings}, year = {2017}, month = {03/2017}, abstract = {

China{\textquoteright}s Design Standard for Energy Efficiency of Public Buildings (the Design Standard) is widely used in the design phase to regulate the energy efficiency of physical assets (envelope, lighting, HVAC) in buildings. However, the standard does not consider many important factors that influence the actual energy use in buildings, and this can lead to gaps between the design estimates and actual energy consumption. To achieve the national energy savings targets defined in the strategic 12th Five-Year Plan, China developed the first standard for energy consumption of buildings GB/T51161-2016 (the Consumption Standard). This study provides an overview of the Consumption Standard, identifies its strengths and weaknesses, and recommends future improvements. The analysis and discussion of the constraint value and the leading value, two key indicators of the energy use intensity, provide insight into the intent and effectiveness of the Consumption Standard. The results indicated that consistency between China{\textquoteright}s Design Standard GB 50189-2015 and the Consumption Standard GB/T51161-2016 could be achieved if the Design Standard used the actual building operations and occupant behavior in calculating the energy use in Chinese buildings. The development of an outcome-based code in the U.S. was discussed in comparison with China{\textquoteright}s Consumption Standard, and this revealed the strengths and challenges associated with implementing a new compliance method based on actual energy use in buildings in the U.S. Overall, this study provides important insights into the latest developments of actual consumption-based building energy standards, and this information should be valuable to building designers and energy policy makers in China and the U.S.

}, keywords = {China, code and standard, energy consumption, energy efficiency, Energy Use Intensity, outcome-based code}, doi = {10.1016/j.enbuild.2017.03.019}, author = {Da Yan and Tianzhen Hong and Cheng Li and Qi Zhang and Jingjing An and shan Hu} } @conference {57409, title = {Application of a stochastic window use model in EnergyPlus}, booktitle = {SimBuild 2012, 5th National Conference of IBPSA-USA, August 1-3, 2012}, year = {2012}, month = {08/2012}, address = {Madison, WI}, abstract = {

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

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

}, url = {https://escholarship.org/uc/item/2gm7r783}, author = {Spencer M. Dutton and Hui Zhang and Yongchao Zhai and Edward A. Arens and Youness Bennani Smires and Samuel L. Brunswick and Kyle S. Konis 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} } @article {242, title = {A Statistical Pattern Analysis Framework for Rooftop Unit Diagnostics}, journal = {International Journal of Heating, Ventilating, Air-Conditioning and Refrigeration Research}, year = {2010}, author = {Massieh Najafi and David M. Auslander and Philip Haves and Michael D. Sohn} } @proceedings {243, title = {Systems Approach to Energy Efficient Building Operation: Case Studies and Lessons Learned in a University Campus}, journal = {2010 ACEEE Summer Study on Energy Efficiency in Buildings}, year = {2010}, publisher = {Omnipress}, address = {Asilomar, California, USA}, abstract = {

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.

}, isbn = {0-918249-60-0}, author = {Satish Narayanan and Michael G. Apte and Philip Haves and Mary Ann Piette and John Elliott} } @article {239, title = {Towards a Very Low Energy Building Stock: Modeling the US Commercial Building Stock to Support Policy and Innovation Planning}, journal = {Building Research and Information}, volume = {37:5}, year = {2009}, chapter = {610}, abstract = {

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

}, author = {Brian E. Coffey and Sam Borgeson and Stephen E. Selkowitz and Joshua S. Apte and Paul A. Mathew and Philip Haves} } @proceedings {247, title = {Application of Machine Learning in Fault Diagnostics of Mechanical Systems}, journal = {International Conference on Modeling, Simulation and Control}, year = {2008}, month = {10/2008}, author = {Massieh Najafi and David M. Auslander and Peter L. Bartlett and Philip Haves} } @proceedings {246, title = {Fault Diagnostics and Supervised Testing: How Fault Diagnostic tools can be Proactive?}, journal = {Eleventh International Conference on Intelligent Systems and Controls}, year = {2008}, month = {11/2008}, abstract = {The topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.}, url = {http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=503}, author = {Massieh Najafi and David M. Auslander and Peter L. Bartlett and Philip Haves} } @proceedings {245, title = {Overcoming the Complexity of Diagnostic Problems due to Sensor Network Architecture}, journal = {Eleventh International Conference on Intelligent Systems and Controls}, year = {2008}, month = {11/08}, abstract = {

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

}, url = {https://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=503}, author = {Massieh Najafi and David M. Auslander and Peter L. Bartlett and Philip Haves} } @conference {2655, title = {Energy Index Evaluation of Buildings in Function of the External Temperature}, booktitle = {Proc. Building Simulation 2007}, year = {2007}, month = {09/2007}, address = {Beijing, China}, author = {Papa, Renata Pietra and Patricia Romeiro da Silva Jota and Assis, Eleonora} } @article {3389, title = {Energy Performance of Underfloor Air Distribution Systems}, year = {2007}, institution = {California Energy Commission - Public Interest Energy Research Program}, author = {Fred S. Bauman and Thomas L. Webster and Hui Jin and Wolfgang Lukaschek and Corinne Benedek and Edward A. Arens and Paul F. Linden and Anna Lui and Walter F. Buhl and Darryl J. Dickerhoff} } @conference {2654, title = {Simulation Enhanced Prototyping of an Experimental Solar House}, booktitle = {Proc. Building Simulation 2007}, year = {2007}, month = {09/2007}, address = {Beijing, China}, author = {Ruchi Choudhary and Godfried Augenbroe and Russell Gentry and Huafen Hu} } @proceedings {396, title = {Carbon Nanotube (CNT)-Centric Thermal Management of Future High Power Microprocessors}, journal = {IEEE CPMT International Symposium and Exhibition on Advanced Packaging Materials}, year = {2006}, month = {03/2006}, address = {Atlanta, GA}, author = {Prajesh Bhattacharya and Wei, X. and Andrei G. Fedorov and Yogendra K. Joshi and Navdeep Bajwa and Anyuan Cao and Pulickel Ajayan} } @conference {2668, title = {Parametric Analysis of a Solar Desiccant Cooling System using the SimSPARK Environment}, booktitle = {IBPSA Building Simulation 2005}, year = {2005}, month = {08/2005}, address = {Montreal, Canada}, author = {Etienne Wurtz and Chadi Maalouf and Laurent Mora and Francis Allard} } @proceedings {49, title = {Comparison of a generalized pattern search and a genetic algorithm optimization method}, journal = {Proc. of the 8th IBPSA Conference}, volume = {III}, year = {2003}, pages = {1401-1408}, address = {Eindhoven, Netherlands}, abstract = {

Building and HVAC system design can significantly improve if numerical optimization is used. However, if a cost function that is smooth in the design parameter is evaluated by a building energy simulation program, it usually becomes replaced with a numerical approximation that is discontinuous in the design parameter. Moreover, many building simulation programs do not allow obtaining an error bound for the numerical approximations to the cost function. Thus, if a cost function is evaluated by such a program, optimization algorithms that depend on smoothness of the cost function can fail far from a minimum.

For such problems it is unclear how the Hooke-Jeeves Generalized Pattern Search optimization algorithm and the simple Genetic Algorithm perform. The Hooke-Jeeves algorithm depends on smoothness of the cost function, whereas the simple Genetic Algorithm may not even converge if the cost function is smooth. Therefore, we are interested in how these algorithms perform if used in conjunction with a cost function evaluated by a building energy simulation program.

In this paper we show what can be expected from the two algorithms and compare their performance in minimizing the annual primary energy consumption of an office building in three locations. The problem has 13 design parameters and the cost function has large discontinuities. The optimization algorithms reduce the energy consumption by 7\% to 32\%, depending on the building location. Given the short labor time to set up the optimization problems, such reductions can yield considerable economic gains.

}, url = {http://www.ibpsa.org/proceedings/BS2003/BS03_1401_1408.pdf}, author = {Michael Wetter and Jonathan A. Wright}, editor = {Godfried Augenbroe and Jan Hensen} } @proceedings {48, title = {A convergent optimization method using pattern search algorithms with adaptive precision simulation}, journal = {Proceedings of the 8th IBPSA Conference}, volume = {III}, year = {2003}, pages = {1393-1400}, address = {Eindhoven, Netherlands}, abstract = {

In solving optimization problems for building design and control, the cost function is often evaluated using a detailed building simulation program. These programs contain code features that cause the cost function to be discontinuous. Optimization algorithms that require smoothness can fail on such problems. Evaluating the cost function is often so time-consuming that stochastic optimization algorithms are run using only a few simulations, which decreases the probability of getting close to a minimum. To show how applicable direct search, stochastic, and gradient-based optimization algorithms are for solving such optimization problems, we compare the performance of these algorithms in minimizing cost functions with different smoothness. We also explain what causes the large discontinuities in the cost functions.

}, keywords = {coordinate search, direct search, genetic algorithm, hooke{\textendash}jeeves, optimization, particle swarm optimization}, author = {Michael Wetter and Elijah Polak}, editor = {Godfried Augenbroe and Jan Hensen} } @conference {55643, title = {Thermal Energy Storage System Sizing}, booktitle = {IBPSA Building Simulation {\textquoteright}89}, year = {1989}, month = {01/1989}, address = {Vancouver, BC, Canada}, url = {http://www.ibpsa.org/proceedings/BS1989/BS89_357_362.pdf}, author = {Dominique Dumortier and Ron C. Kammerud and Birdsall, Bruce E. and Brandt Andersson and Joseph H. Eto and William L. Carroll and Frederick C. Winkelmann} }