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

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

1 aChen, Chien-fei1 aHong, Tianzhen1 ade Rubens, Gerardo, Zarazua1 aYilmaz, Selin1 aBandurski, Karol1 aBélafi, Zsófia, Deme1 aDe Simone, Marilena1 aBavaresco, Mateus, Vinícius1 aWang, Yu1 aLiu, Pei-ling1 aBarthelmes, Verena, M.1 aAdams, Jacqueline1 aD'Oca, Simona1 aPrzybylski, Łukasz uhttps://simulationresearch.lbl.gov/publications/culture-conformity-and-carbon-multi02860nas a2200253 4500008004100000022001300041245017500054210006900229260001200298300001400310490000800324520184700332653002402179653000902203653002902212653002602241100001602267700001502283700001802298700002202316700002002338700001502358856023302373 2019 eng d a0306261900aPractical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems0 aPractical factors of envelope model setup and their effects on t c02/2019 a410 - 4250 v2363 a

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

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

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

10abenchmarking10abuilding simulation10aModel predictive control10asoftware development1 aBlum, David1 aJorissen, Filip1 aHuang, Sen1 aChen, Yan1 aArroyo, Javier1 aBenne, Kyle1 aLi, Yanfei1 aGavan, Valentin1 aRivalin, Lisa1 aHelsen, Lieve1 aVrabie, Draguna1 aWetter, Michael1 aSofos, Marina uhttps://simulationresearch.lbl.gov/publications/prototyping-boptest-framework02762nas a2200241 4500008004100000022001300041245011200054210006900166260001200235300001400247490000800261520184900269653002602118653002102144653002402165653000902189653002502198653001602223100001702239700001202256700001902268856023302287 2018 eng d a0378778800aClustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings0 aClustering and statistical analyses of airconditioning intensity c09/2018 a214 - 2270 v1743 a

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.

10aAC usage benchmarking10aAir-conditioning10aClustering analysis10aKPIs10aresidential building10aUse pattern1 aAn, Jingjing1 aYan, Da1 aHong, Tianzhen uhttps://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/plain01954nas a2200241 4500008004100000245009500041210006900136490000800205520119700213653002201410653001201432653002301444653002201467653002101489653002501510100001401535700001201549700001701561700001901578700001401597700001401611856008701625 2018 eng d00aComparative Study of Air-Conditioning Energy Use of Four Office Buildings in China and USA0 aComparative Study of AirConditioning Energy Use of Four Office B0 v1693 a

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.

10aBuilding envelope10aclimate10aenergy consumption10aoccupant behavior10aoffice buildings10atechnological choice1 aZhou, Xin1 aYan, Da1 aAn, Jingjing1 aHong, Tianzhen1 aShi, Xing1 aJin, Xing uhttps://simulationresearch.lbl.gov/publications/comparative-study-air-conditioning02543nas a2200277 4500008004100000022001300041245018600054210006900240260001200309300001400321490000800335520142800343653002701771653002701798653001801825653003601843100001901879700002001898700002201918700002101940700002401961700001301985700001501998700001902013856023302032 2018 eng d a0306261900aTranslating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California0 aTranslating climate change and heating system electrification im c09/2018 a522 - 5340 v2253 a

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 30–40% 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.

10aBuilding Energy Demand10aClimate Change Impacts10aelectric grid10aHeating Electrification Effects1 aTarroja, Brian1 aChiang, Felicia1 aAghaKouchak, Amir1 aSamuelsen, Scott1 aRaghavan, Shuba, V.1 aWei, Max1 aSun, Kaiyu1 aHong, Tianzhen uhttps://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/plain02095nas a2200277 4500008003900000022001300039245010600052210006900158260001200227300001400239490000800253520125200261653001701513653001801530653001701548653001501565653001301580653001501593100002601608700002001634700002101654700002001675700001901695700001801714856008501732 2017 d a0378778800aEnergy saving potential of a two-pipe system for simultaneous heating and cooling of office buildings0 aEnergy saving potential of a twopipe system for simultaneous hea c01/2017 a234 - 2470 v1343 a

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

10aactive beams10aenergy saving10aHVAC systems10alow-exergy10amodelica10asimulation1 aMaccarini, Alessandro1 aWetter, Michael1 aAfshari, Alireza1 aHultmark, Goran1 aBergsoe, Niels1 aVorre, Anders uhttps://simulationresearch.lbl.gov/publications/energy-saving-potential-two-pipe02155nas a2200133 4500008004100000245009000041210006900131520167300200100001701873700001201890700001901902700001501921856008501936 2017 eng d00aA Novel Stochastic Modeling Method to Simulate Cooling Loads in Residential Districts0 aNovel Stochastic Modeling Method to Simulate Cooling Loads in Re3 a

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.

1 aAn, Jingjing1 aYan, Da1 aHong, Tianzhen1 aSun, Kaiyu uhttps://simulationresearch.lbl.gov/publications/novel-stochastic-modeling-method02293nas a2200241 4500008004100000245016600041210006900207520145100276653002201727653001801749653002101767653002301788653002501811653002201836653001501858100001401873700001201887700001701899700001901916700001401935700001501949856008701964 2017 eng d00aSpatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings0 aSpatial Distribution of Internal Heat Gains A Probabilistic Repr3 a

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

10aair handling unit10achiller plant10aequipment sizing10ainternal heat gain10aspatial distribution10aspatial diversity10astochastic1 aZhang, Qi1 aYan, Da1 aAn, Jingjing1 aHong, Tianzhen1 aTian, Wei1 aSun, Kaiyu uhttps://simulationresearch.lbl.gov/publications/spatial-distribution-internal-heat02540nas a2200241 4500008004100000245008400041210006900125260001200194520178100206653001001987653002201997653002302019653002202042653002502064653002302089100001202112700001902124700001402143700001402157700001702171700001302188856009702201 2017 eng d00aA Thorough Assessment of China’s Standard for Energy Consumption of Buildings0 aThorough Assessment of China s Standard for Energy Consumption o c03/20173 a

China’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’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’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.

10aChina10acode and standard10aenergy consumption10aenergy efficiency10aEnergy Use Intensity10aoutcome-based code1 aYan, Da1 aHong, Tianzhen1 aLi, Cheng1 aZhang, Qi1 aAn, Jingjing1 aHu, shan uhttps://simulationresearch.lbl.gov/publications/thorough-assessment-china%E2%80%99s-standard01318nas a2200193 4500008003900000245006300039210006300102260002500165520071500190100002400905700001500929700001900944700002200963700002900985700002601014700002001040700001801060856004601078 2012 d00aApplication of a stochastic window use model in EnergyPlus0 aApplication of a stochastic window use model in EnergyPlus aMadison, WIc08/20123 a

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

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

1 aDutton, Spencer, M.1 aZhang, Hui1 aZhai, Yongchao1 aArens, Edward, A.1 aSmires, Youness, Bennani1 aBrunswick, Samuel, L.1 aKonis, Kyle, S.1 aHaves, Philip uhttps://escholarship.org/uc/item/2gm7r78301850nas 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-constraints00472nas a2200121 4500008004100000245007400041210006900115100002000184700002500204700001800229700002200247856008100269 2010 eng d00aA Statistical Pattern Analysis Framework for Rooftop Unit Diagnostics0 aStatistical Pattern Analysis Framework for Rooftop Unit Diagnost1 aNajafi, Massieh1 aAuslander, David, M.1 aHaves, Philip1 aSohn, Michael, D. uhttps://simulationresearch.lbl.gov/publications/statistical-pattern-analysis02269nas a2200169 4500008004100000020001800041245011700059210006900176260004100245520162500286100002201911700002201933700001801955700002201973700001801995856008602013 2010 eng d a0-918249-60-000aSystems Approach to Energy Efficient Building Operation: Case Studies and Lessons Learned in a University Campus0 aSystems Approach to Energy Efficient Building Operation Case Stu aAsilomar, California, USAbOmnipress3 a

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

1 aNarayanan, Satish1 aApte, Michael, G.1 aHaves, Philip1 aPiette, Mary, Ann1 aElliott, John uhttps://simulationresearch.lbl.gov/publications/systems-approach-energy-efficient01592nas a2200169 4500008004100000245013000041210006900171490000900240520096100249100002201210700001801232700002701250700002101277700002101298700001801319856008501337 2009 eng d00aTowards a Very Low Energy Building Stock: Modeling the US Commercial Building Stock to Support Policy and Innovation Planning0 aTowards a Very Low Energy Building Stock Modeling the US Commerc0 v37:53 a

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

1 aCoffey, Brian, E.1 aBorgeson, Sam1 aSelkowitz, Stephen, E.1 aApte, Joshua, S.1 aMathew, Paul, A.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/towards-very-low-energy-building00509nas a2200133 4500008004100000245007900041210006900120260001200189100002000201700002500221700002400246700001800270856008700288 2008 eng d00aApplication of Machine Learning in Fault Diagnostics of Mechanical Systems0 aApplication of Machine Learning in Fault Diagnostics of Mechanic c10/20081 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/application-machine-learning-fault01212nas a2200145 4500008004100000245009100041210006900132260001200201520069300213100002000906700002500926700002400951700001800975856007300993 2008 eng d00aFault Diagnostics and Supervised Testing: How Fault Diagnostic tools can be Proactive?0 aFault Diagnostics and Supervised Testing How Fault Diagnostic to c11/20083 aThe topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.1 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttp://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=50301442nas a2200145 4500008004100000245008800041210006900129260001000198520092700208100002001135700002501155700002401180700001801204856007401222 2008 eng d00aOvercoming the Complexity of Diagnostic Problems due to Sensor Network Architecture0 aOvercoming the Complexity of Diagnostic Problems due to Sensor N c11/083 a

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

1 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttps://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=50300503nas a2200121 4500008004100000245008100041210006900122260002800191100002500219700003100244700002000275856008600295 2007 eng d00aEnergy Index Evaluation of Buildings in Function of the External Temperature0 aEnergy Index Evaluation of Buildings in Function of the External aBeijing, Chinac09/20071 aPapa, Renata, Pietra1 aJota, Patricia, Romeiro da1 aAssis, Eleonora uhttps://simulationresearch.lbl.gov/publications/energy-index-evaluation-buildings00740nas a2200205 4500008004100000245006200041210006200103260007500165100002100240700002400261700001300285700002400298700002100322700002200343700002100365700001400386700002100400700002700421856008600448 2007 eng d00aEnergy Performance of Underfloor Air Distribution Systems0 aEnergy Performance of Underfloor Air Distribution Systems bCalifornia Energy Commission - Public Interest Energy Research Program1 aBauman, Fred, S.1 aWebster, Thomas, L.1 aJin, Hui1 aLukaschek, Wolfgang1 aBenedek, Corinne1 aArens, Edward, A.1 aLinden, Paul, F.1 aLui, Anna1 aBuhl, Walter, F.1 aDickerhoff, Darryl, J. uhttps://simulationresearch.lbl.gov/publications/energy-performance-underfloor-air00501nas a2200133 4500008004100000245006700041210006700108260002800175100002100203700002400224700002000248700001500268856008400283 2007 eng d00aSimulation Enhanced Prototyping of an Experimental Solar House0 aSimulation Enhanced Prototyping of an Experimental Solar House aBeijing, Chinac09/20071 aChoudhary, Ruchi1 aAugenbroe, Godfried1 aGentry, Russell1 aHu, Huafen uhttps://simulationresearch.lbl.gov/publications/simulation-enhanced-prototyping00625nas a2200169 4500008004100000245009000041210006900131260002500200100002600225700001200251700002400263700002400287700001900311700001600330700002100346856008800367 2006 eng d00aCarbon Nanotube (CNT)-Centric Thermal Management of Future High Power Microprocessors0 aCarbon Nanotube CNTCentric Thermal Management of Future High Pow aAtlanta, GAc03/20061 aBhattacharya, Prajesh1 aWei, X.1 aFedorov, Andrei, G.1 aJoshi, Yogendra, K.1 aBajwa, Navdeep1 aCao, Anyuan1 aAjayan, Pulickel uhttps://simulationresearch.lbl.gov/publications/carbon-nanotube-cnt-centric-thermal00529nas a2200133 4500008004100000245009100041210006900132260003000201100001900231700001900250700001800269700002000287856008800307 2005 eng d00aParametric Analysis of a Solar Desiccant Cooling System using the SimSPARK Environment0 aParametric Analysis of a Solar Desiccant Cooling System using th aMontreal, Canadac08/20051 aWurtz, Etienne1 aMaalouf, Chadi1 aMora, Laurent1 aAllard, Francis uhttps://simulationresearch.lbl.gov/publications/parametric-analysis-solar-desiccant02172nas a2200169 4500008004100000245009100041210006900132260002700201300001400228490000800242520160400250100002001854700002501874700002401899700001601923856006301939 2003 eng d00aComparison of a generalized pattern search and a genetic algorithm optimization method0 aComparison of a generalized pattern search and a genetic algorit aEindhoven, Netherlands a1401-14080 vIII3 a

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.

1 aWetter, Michael1 aWright, Jonathan, A.1 aAugenbroe, Godfried1 aHensen, Jan uhttp://www.ibpsa.org/proceedings/BS2003/BS03_1401_1408.pdf01634nas a2200241 4500008004100000245010400041210006900145260002700214300001400241490000800255520083600263653002201099653001801121653002201139653001901161653001701180653003201197100002001229700001801249700002401267700001601291856008501307 2003 eng d00aA convergent optimization method using pattern search algorithms with adaptive precision simulation0 aconvergent optimization method using pattern search algorithms w aEindhoven, Netherlands a1393-14000 vIII3 a

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.

10acoordinate search10adirect search10agenetic algorithm10ahooke–jeeves10aoptimization10aparticle swarm optimization1 aWetter, Michael1 aPolak, Elijah1 aAugenbroe, Godfried1 aHensen, Jan uhttps://simulationresearch.lbl.gov/publications/convergent-optimization-method-000555nas a2200169 4500008003900000245004100039210004100080260003500121100002500156700002200181700002400203700002200227700002000249700002500269700003000294856006100324 1989 d00aThermal Energy Storage System Sizing0 aThermal Energy Storage System Sizing aVancouver, BC, Canadac01/19891 aDumortier, Dominique1 aKammerud, Ron, C.1 aBirdsall, Bruce, E.1 aAndersson, Brandt1 aEto, Joseph, H.1 aCarroll, William, L.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1989/BS89_357_362.pdf