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-multi02298nas a2200277 4500008004100000022001300041245016000054210006900214260001600283300001600299490000800315520136500323653004401688653002401732653002301756653001801779653002201797653002301819100001201842700001701854700001501871700001901886700001301905700001501918856008701933 2019 eng d a0048969700aAssessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California0 aAssessment of occupantbehaviorbased indoor air quality and its i cJan-10-2019 a1251 - 12610 v6863 a

The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires—by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviours by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires

10acomputational fluid dynamics siumlation10ahuman exposure risk10aindoor air quality10aNAPA wildfire10aoccupant behavior10arespiratory injury1 aLuo, Na1 aWeng, Wenguo1 aXu, Xiaoyu1 aHong, Tianzhen1 aFu, Ming1 aSun, Kaiyu uhttps://simulationresearch.lbl.gov/publications/assessment-occupant-behavior-based01804nas a2200265 4500008004100000022001300041245010900054210006900163260001600232300001100248490000800259520098000267653001601247653002201263653002101285653002501306653002401331100001401355700001901369700001301388700001701401700001601418700001901434856008501453 2019 eng d a0360132300aCross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering0 aCrosssource sensing data fusion for building occupancy predictio cJan-09-2019 a1062800 v1623 a

Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.

10adata fusion10aFeature selection10aMachine learning10aoccupancy prediction10aPhysics-based model1 aWang, Wei1 aHong, Tianzhen1 aXu, Ning1 aXu, Xiaodong1 aChen, Jiayu1 aShan, Xiaofang uhttps://simulationresearch.lbl.gov/publications/cross-source-sensing-data-fusion02297nas a2200241 4500008004100000022001300041245010400054210006900158260001200227300001400239490000800253520153900261653001601800653001801816653002401834653003301858653001901891653002301910100001401933700001901947700002201966856006701988 2019 eng d a0306261900aData fusion in predicting internal heat gains for office buildings through a deep learning approach0 aData fusion in predicting internal heat gains for office buildin c02/2019 a386 - 3980 v2403 a

Heating, Ventilation, and Air Conditioning (HVAC) is a major energy consumer in buildings. The predictive control has demonstrated a potential to reduce HVAC energy use. To facilitate predictive HVAC control, internal heat gains prediction is required. In this study, we applied Long Short-Term Memory Networks, a special form of deep neural network, to predict miscellaneous electric loads, lighting loads, occupant counts and internal heat gains in two United States office buildings. Compared with the predetermined schedules used in American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 90.1, the Long Short-Term Memory Networks method could reduce the prediction errors of internal heat gains from 12% to 8% in Building A, and from 26% to 16% in Building B. It was also found that for internal heat gains prediction, miscellaneous electric loads is a more important feature than occupant counts for two reasons. First, miscellaneous electric loads is the best proxy variable for internal heat gains, as it is the major component of and has the highest correlation coefficient with the internal heat gains. Second, miscellaneous electric loads contain valuable information to predict occupant count, while occupant count could not help improve miscellaneous electric loads prediction. These findings could help researchers and practitioners select the most relevant features to more accurately predict internal heat gains for the implementation of predictive HVAC control in buildings.

10adata fusion10adeep learning10aInternal heat gains10aMiscellaneous electric loads10aOccupant count10aPredictive control1 aWang, Zhe1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://linkinghub.elsevier.com/retrieve/pii/S030626191930363002259nas a2200265 4500008004100000022001300041245013000054210006900184260001200253300001400265490000800279520141300287653002901700653002101729653002701750653001901777653003601796100001401832700001901846700001701865700001601882700001501898700001301913856006701926 2019 eng d a0306261900aForecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm0 aForecasting districtscale energy dynamics through integrating bu c04/2019 a217 - 2300 v2483 a

With the development of data-driven techniques, district-scale building energy prediction has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m2, compared to 12.05% and 0.63 kWh/m2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m2 for the support vector regression model.

10aBuilding Energy Modeling10aBuilding network10aData-driven prediction10aDistrict-scale10aLong short-term memory networks1 aWang, Wei1 aHong, Tianzhen1 aXu, Xiaodong1 aChen, Jiayu1 aLiu, Ziang1 aXu, Ning uhttps://linkinghub.elsevier.com/retrieve/pii/S030626191930749402515nas a2200241 4500008004100000022001300041245010300054210006900157260001200226300001200238490000800250520174400258653003102002653002102033653003902054653002602093653002102119100001702140700001402157700001902171700001602190856006702206 2019 eng d a0378778800aIncorporating machine learning with building network analysis to predict multi-building energy use0 aIncorporating machine learning with building network analysis to c06/2019 a80 - 970 v1863 a

Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.

10aArtificial neural networks10aBuilding network10acold winter and hot summer climate10aEnergy use prediction10aMachine learning1 aXu, Xiaodong1 aWang, Wei1 aHong, Tianzhen1 aChen, Jiayu uhttps://linkinghub.elsevier.com/retrieve/pii/S037877881831976502420nas a2200253 4500008004100000022001300041245008400054210006900138260001200207300001400219490000800233520166500241653002101906653002101927653002501948653001901973653001801992653001502010100001402025700001902039700002202058700001902080856006702099 2019 eng d a0360132300aInferring occupant counts from Wi-Fi data in buildings through machine learning0 aInferring occupant counts from WiFi data in buildings through ma c05/2019 a281 - 2940 v1583 a

An important approach to curtail building energy consumption is to optimize building control based on occupancy information. Various studies proposed to estimate occupant counts through different approaches and sensors. However, high cost and privacy concerns remain as major barriers, restricting the practice of occupant count detection. In this study, we propose a novel method utilizing data from widely deployed Wi-Fi infrastructure to infer occupant counts through machine learning. Compared with the current indirect measurement methods, our method improves the performance of estimating people count: (1) we avoid privacy concerns by anonymizing and reshuffling the MAC addresses on a daily basis; (2) we adopted a heuristic feature engineer approach to cluster connected devices into different types based on their daily connection duration. We tested the method in an office building located in California. In an area with an average occupancy of 22–27 people and a peak occupancy of 48–74 people, the root square mean error on the test set is less than four people. The error is within two people counts for more than 70% of estimations, and less than six counts for more than 90% of estimations, indicating a relatively high accuracy. The major contribution of this study is proposing a novel and accurate approach to detect occupant counts in a non-intrusive way, i.e., utilizing existing Wi-Fi infrastructure in buildings without requiring the installation of extra hardware or sensors. The method we proposed is generic and could be applied to other commercial buildings to infer occupant counts for energy efficient building control.

10aBuilding control10aMachine learning10aOccupancy estimation10aOccupant count10aRandom forest10aWi-Fi data1 aWang, Zhe1 aHong, Tianzhen1 aPiette, Mary, Ann1 aPritoni, Marco uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930333601978nas a2200145 4500008004100000022001300041245012900054210006900183260001600252300001100268520142500279100001401704700001901718856009501737 2019 eng d a1364032100aLearning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States0 aLearning occupants indoor comfort temperature through a Bayesian cJan-11-2019 a1095933 a

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4°C for the cooling mode with a median of 23.7°C, and between 20.5 and 24.9°C for the heating mode with a median of 22.7°C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2-3°C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

1 aWang, Zhe1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/learning-occupants%E2%80%99-indoor-comfort02424nas a2200241 4500008004100000022001300041245013600054210006900190260001200259300001200271490000800283520161400291653002301905653003401928653002301962653002701985100001402012700001902026700001202045700001902057700001602076856009002092 2019 eng d a0306261900aLinking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification0 aLinking energycyberphysical systems with occupancy prediction an c02/2019 a55 - 690 v2363 a

With rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most prominent platforms to improve building design and management. Researchers investigated the possibility of integrating energy management system with cyber-physical systems as energy-cyber-physical systems to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is one major source of uncertainties for energy management, ignoring it often results in energy wastes caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposed an occupancy linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilized ensemble classification algorithms to extract three types of occupancy information. It creates a data interface to link energy management system and cyber-physical systems and allows automated occupancy detection and interpretation through assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy type, the proposed model can potentially save about 26.4% of energy consumption from the cooling and ventilation demands.

10aBuilding occupancy10aEnergy-Cyber-Physical Systems10aensemble algorithm10aWi-Fi probe technology1 aWang, Wei1 aHong, Tianzhen1 aLi, Nan1 aWang, Ryan, Qi1 aChen, Jiayu uhttps://simulationresearch.lbl.gov/publications/linking-energy-cyber-physical-systems02290nas a2200193 4500008004100000022001400041245012000055210006900175260001200244300001400256490000700270520148300277100001701760700001401777700001401791700001901805700001301824856025901837 2019 eng d a1996-359900aPerformance-driven optimization of urban open space configuration in the cold-winter and hot-summer region of China0 aPerformancedriven optimization of urban open space configuration c03/2019 a411 - 4240 v123 a

Urbanization has led to changes in urban morphology and climate, while urban open space has become an important ecological factor for evaluating the performance of urban development. This study presents an optimization approach using computational performance simulation. With a genetic algorithm using the Grasshopper tool, this study analyzed the layout and configuration of urban open space and its impact on the urban micro-climate under summer and winter conditions. The outdoor mean Universal Thermal Climate Index (UTCI) was applied as the performance indicator for evaluating the quality of the urban micro-climate. Two cases—one testbed and one real urban block in Nanjing, China—were used to validate the computer-aided simulation process. The optimization results in the testbed showed UTCI values varied from 36.5 to 37.3 °C in summer and from −4.9 to −1.9 °C in winter. In the case of the real urban block, optimization results show, for summer, although the average UTCI value increased by 0.6 °C, the average air velocity increased by 0.2 m/s; while in winter, the average UTCI value increased by 1.7 °C and the average air velocity decreased by 0.2 m/s. These results demonstrate that the proposed computer-aided optimization process can improve the thermal comfort conditions of open space in urban blocks. Finally, this study discusses strategies and guidelines for the layout design of urban open space to improve urban environment comfort.

1 aXu, Xiaodong1 aWu, Yifan1 aWang, Wei1 aHong, Tianzhen1 aXu, Ning uhttp://link.springer.com/10.1007/s12273-019-0510-zhttp://link.springer.com/content/pdf/10.1007/s12273-019-0510-z.pdfhttp://link.springer.com/content/pdf/10.1007/s12273-019-0510-z.pdfhttp://link.springer.com/article/10.1007/s12273-019-0510-z/fulltext.html02860nas 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/plain02376nas a2200241 4500008004100000022001300041245008400054210006900138260001200207300001200219490000800231520164800239653001801887653003501905653001901940653001501959653001501974653002301989100001402012700001902026700002202045856006702067 2019 eng d a0360544200aPredicting plug loads with occupant count data through a deep learning approach0 aPredicting plug loads with occupant count data through a deep le c05/2019 a29 - 420 v1813 a

Predictive control has gained increasing attention for its ability to reduce energy consumption and improve occupant comfort in buildings. The plug loads prediction is a key component for the predictive building controls, as plug loads is a major source of internal heat gains in buildings. This study proposed a novel method to apply the Long-Short-Term-Memory (LSTM) Network, a special form of Recurrent Neural Network, to predict plug loads. The occupant count and the time have been confirmed to drive the plug load profile and thus selected as the features for the plug load prediction. The LSTM network was trained and tested with ground truth occupant count data collected from a real office building in Berkeley, California. Results from the LSTM network markedly improve the prediction accuracy compared with traditional linear regression methods and the classical Artificial Neural Network. 95% of 1-h predictions from LSTM network are within ±1 kW of the actual plug loads, given the average plug loads during the office hour is 8.6 kW. The CV(RMSE) of the predicted plug load is 11% for the next hour, and 20% for the next 8 h. Lastly, we compared four prediction approaches with the office building we monitored: LSTM vs. ARIMA, with occupant counts vs. without occupant counts. It was found, the prediction error of the LSTM approach is around 4% less than the ARIMA approach. Using occupant counts as an exogenous input could further reduce the prediction error by 5%–6%. The findings of this paper could shed light on the plug load prediction for building control optimizations such as model-predictive control.

10adeep learning10aLong short term memory network10aOccupant count10aPlug loads10aprediction10aPredictive control1 aWang, Zhe1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://linkinghub.elsevier.com/retrieve/pii/S036054421931020502188nas 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-framework00428nas a2200133 4500008004100000245003700041210003700078260001200115653003300127653001300160100002000173700001600193856008500209 2019 eng d00aQuayside Energy Systems Analysis0 aQuayside Energy Systems Analysis c03/201910aDistrict Heating and cooling10amodelica1 aWetter, Michael1 aHu, Jianjun uhttps://simulationresearch.lbl.gov/publications/quayside-energy-systems-analysis01789nas a2200205 4500008004100000245013500041210006900176260001600245300000900261490000700270520100200277653012101279100001701400700001801417700001401435700001301449700001901462700001101481856009101492 2019 eng d00aRevealing Urban Morphology and Outdoor Comfort through Genetic Algorithm-Driven Urban Block Design in Dry and Hot Regions of China0 aRevealing Urban Morphology and Outdoor Comfort through Genetic A cJan-07-2019 a36830 v113 a

In areas with a dry and hot climate, factors such as strong solar radiation, high temperature, low humidity, dazzling light, and dust storms can tremendously reduce people’s thermal comfort. Therefore, researchers are paying more attention to outdoor thermal comfort in urban environments as part of urban design. This study proposed an automatic workflow to optimize urban spatial forms with the aim of improvement of outdoor thermal comfort conditions, characterized by the universal thermal climate index (UTCI). A city with a dry and hot climate—Kashgar, China—is further selected as an actual case study of an urban block and Rhino & Grasshopper is the platform used to conduct simulation and optimization process with the genetic algorithm. Results showed that in summer, the proposed method can reduce the averaged UTCI from 31.17 to 27.43 °C, a decrease of about 3.74 °C, and reduce mean radiation temperature (MRT) from 43.94 to 41.29 °C, a decrease of about 2.65 °C.

10adry and hot areas; outdoor thermal comfort; urban morphology; urban performance simulation; genetic algorithm-driven1 aXu, Xiaodong1 aYin, Chenhuan1 aWang, Wei1 aXu, Ning1 aHong, Tianzhen1 aLi, Qi uhttps://www.mdpi.com/2071-1050/11/13/3683https://www.mdpi.com/2071-1050/11/13/3683/pdf01980nas a2200277 4500008004100000022001300041245009800054210006900152260001200221300001400233490000800247520110300255653002201358653004301380653002401423653002601447653003401473653002101507653002001528100001401548700002201562700001601584700001601600700001901616856006701635 2019 eng d a0360132300aThe Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes0 aSqueaky wheel Machine learning for anomaly detection in subjecti c03/2019 a219 - 2270 v1513 a

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

10aanomaly detection10aASHRAE global thermal comfort database10aK-nearest neighbors10aMultivariate Gaussian10aOccupancy responsive controls10aSubjective votes10athermal comfort1 aWang, Zhe1 aParkinson, Thomas1 aLi, Peixian1 aLin, Borong1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930086100542nas a2200133 4500008004100000245013400041210006900175490000800244100001900252700002000271700001800291700001700309856008200326 2018 eng d00aBidirectional low temperature district energy systems with agent-based control: Performance comparison and operation optimization0 aBidirectional low temperature district energy systems with agent0 v2091 aBunning, Felix1 aWetter, Michael1 aFuchs, Marcus1 aMuller, Dirk uhttps://simulationresearch.lbl.gov/publications/bidirectional-low-temperature01905nas a2200217 4500008004100000022001400041245009000055210006900145260001200214300001100226520113500237653001301372653003101385653003101416653002301447653003301470100001401503700001901517700001501536856013601551 2018 eng d a1940-149300aBuildings.Occupants: a Modelica package for modelling occupant behaviour in buildings0 aBuildingsOccupants a Modelica package for modelling occupant beh c11/2018 a1 - 123 a

Energy-related occupant behaviour is crucial to design and operation of energy and control systems in buildings. Occupant behaviours are often oversimplified as static schedules or settings in building performance simulation ignoring their stochastic nature. The continuous and dynamic interaction between occupants and building systems motivates their simultaneous simulation in an efficient manner. In the past, simultaneous simulation has relied on co-simulation approaches or customized source code changes to building simulation programmes. This paper presents Buildings. Occupants, an open-source package implemented in Modelica, for the simulation of occupant behaviours of lighting, windows, blinds, heating and air conditioning systems in office and residential buildings. Examples were presented to illustrate how the models in the Occupants package are capable to simulate stochastic occupant behaviours. The major contribution of this work is to introduce the equation-based modelling approach to simulate occupant behaviours in buildings and to develop an open-source Occupants package in the Modelica language

10amodelica10aModelica Buildings Library10aModelica Occupants Package10aOccupant Behaviour10aOccupant behaviour modelling1 aWang, Zhe1 aHong, Tianzhen1 aJia, Ruoxi uhttps://www.tandfonline.com/doi/full/10.1080/19401493.2018.1543352https://www.tandfonline.com/doi/pdf/10.1080/19401493.2018.154335202289nas a2200169 4500008004100000245003300041210003300074260001200107520180900119653001401928653001301942653000901955100002001964700002101984700001602005856009802021 2018 eng d00aControl Description Language0 aControl Description Language c08/20183 a

Properly designed and implemented building control sequences can significantly reduce energy consumption. However, there is currently no process with supporting tools that allows the assessment of the performance of different control sequences, export the control sequences in a vendor-neutral format for cost estimation and for implementation on a building automation system through machine-to-machine translation, and reuse the sequences for verification during commissioning.

This paper describes a Control Description Language (CDL) that we developed to create such a process. For CDL, we selected a subset of Modelica that allows a convenient representation of control sequences, simulation of the control sequence coupled to a building energy model, and development of translators from CDL to building automation systems. To aid in the development of such translators, we created a translator from CDL to a JSON intermediate format. In future work, we seek to work with building control providers to develop translators from CDL to commercial building automation systems.

Through a case study, we show that CDL suffices for simulation-based performance assessment of two ASHRAE-published control sequences for a variable air volume flow system of an office building. Moreover, the case study showed that merely due to differences in the control sequences, annual HVAC energy use was reduced by 30%. This difference is larger than the accuracy required when comparing different HVAC systems, thereby questioning the current practice of idealizing control sequences in building energy simulations, and demonstrating the importance of ensuring that the control sequence used during design simulations corresponds to the control sequence that will be implemented in the real building

10abuildings10acontrols10ahvac1 aWetter, Michael1 aGrahovac, Milica1 aHu, Jianjun uhttps://simulationresearch.lbl.gov/wetter/download/2018-americanModelica-WetterGrahovacHu.pdf03134nas a2200241 4500008004100000022001300041245011500054210006900169260001200238300001400250490000800264520230200272653005802574653001602632653002102648653002102669653004902690653001302739100001802752700001802770700002102788856008302809 2018 eng d a0378778800aEfficient modeling of optically-complex, non-coplanar exterior shading: Validation of matrix algebraic methods0 aEfficient modeling of opticallycomplex noncoplanar exterior shad c09/2018 a464 - 4830 v1743 a

It has long been established that shading windows with overhangs, fins, and other types of non-coplanar systems (NCS) is one of the most effective ways of controlling solar heat gains in buildings because they intercept solar radiation prior to entry into the building. Designers however often specify non-opaque materials (e.g., louvers, fritted glass, expanded metal mesh) for these systems in order to admit daylight, reduce lighting energy use, and improve indoor environmental quality. Most simulation tools rely on geometric calculations and radiosity methods to model the solar heat gain impacts of NCS and cannot model optically-complex materials or geometries. For daylighting analysis, optically-complex NCS can be modeled using matrix algebraic methods, although time-efficient parametric analysis has not yet been implemented. Determining the best design and/or material for static or operable NCS that minimize cooling, heating, and lighting energy use and peak demand requires an iterative process. This study describes and validates a matrix algebraic method that enables parametric energy analysis of NCS. Such capabilities would be useful not only for design but also for development of prescriptive energy-efficiency standards, rating and labeling systems for commercial products, development of design guidelines, and development of more optimal NCS technologies.

A facade or "F" matrix, which maps the transfer of flux from the NCS to the surface of the window, is introduced and its use is explained. A field study was conducted in a full-scale outdoor testbed to measure the daylight performance of an operable drop-arm awning. Simulated data were compared to measured data in order to validate the models. Results demonstrated model accuracy: simulated workplane illuminance was within 11-13%, surface luminance was within 16-18%, and the daylight glare probability was within 6-9% of measured results. Methods used to achieve accurate results are discussed. Results of the validation of daylighting performance are applicable to solar heat gain performance. Since exterior shading can also significantly reduce peak demand, these models enable stakeholders to more accurately assess HVAC and lighting impacts in support of grid management and resiliency goals.

10abidirectional scattering distribution function (BSDF)10adaylighting10aexterior shading10asolar heat gains10avalidation; building energy simulation tools10awindows.1 aWang, Taoning1 aWard, Gregory1 aLee, Eleanor, S. uhttps://www.sciencedirect.com/science/article/pii/S0378778818302457?via%3Dihub02433nas a2200193 4500008004100000245009600041210006900137490000800206520175100214653002201965653001501987653004302002653002702045653002802072100001402100700001602114700001902130856009002149 2018 eng d00aModeling occupancy distribution in large spaces with multi-feature classification algorithm0 aModeling occupancy distribution in large spaces with multifeatur0 v1373 a

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

10aenergy efficiency10aHVAC loads10amulti-feature classification algorithm10aoccupancy distribution10aoccupancy-based control1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-occupancy-distribution-large02356nas a2200229 4500008004100000022001300041245012200054210006900176260001200245300001400257490000700271520146000278653001601738653002601754653002101780653002501801653001801826100001401844700001601858700001901874856023301893 2018 eng d a0926580500aOccupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings0 aOccupancy prediction through machine learning and data fusion of c10/2018 a233 - 2430 v943 a

Occupancy information is crucial to building facility design, operation, and energy efficiency. Many studies propose the use of environmental sensors (such as carbon dioxide, air temperature, and relative humidity sensors) and radio-frequency sensors (Wi-Fi networks) to monitor, assess, and predict occupancy information for buildings. As many methods have been developed and a variety of sensory data sources are available, establishing a proper selection of model and data source is critical to the successful implementation of occupancy prediction systems. This study compared three popular machine learning algorithms, including k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN), combined with three data sources, including environmental data, Wi-Fi data, and fused data, to optimize the occupancy models' performance in various scenarios. Three error measurement metrics, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE), have been employed to compare the models' accuracies. Examined with an on-site experiment, the results suggest that the ANN-based model with fused data has the best performance, while the SVM model is more suitable with Wi-Fi data. The results also indicate that, comparing with independent data sources, the fused data set does not necessarily improve model accuracy but shows a better robustness for occupancy prediction.

10adata fusion10aenvironmental sensing10aMachine learning10aoccupancy prediction10aWi-Fi sensing1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S0926580518302656https://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/plain02098nas a2200181 4500008004100000022001300041245012600054210006900180260001200249300001400261490000800275520133900283100001401622700001601636700001901652700001201671856023301683 2018 eng d a0360132300aOccupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology0 aOccupancy prediction through Markov based feedback recurrent neu c06/2018 a160 - 1700 v1383 a

Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings' exiting WiFi infrastructures have been widely studied in the research of occupancy and energy conservation. However, using WiFi to assess occupancy is challenging due to that occupancy information is often characterized stochastically and varies with time and easily disturbed by building components. To overcome such limitations, this study utilizes WiFi probe technology to actively scan WiFi connection requests and responses between access points and network devices of building occupants. With captured signals, this study proposed a Markov based feedback recurrent neural network (M-FRNN) algorithm to model and predict the occupancy profiles. One on-site experiment was conducted to collect ground truth data using camera-based video analysis and the results were used to validate the M-FRNN occupancy prediction model over a 9-day measurement period. From the results, the M-FRNN based occupancy model using WiFi probes shows best accuracies can reach 80.9%, 89.6%, and 93.9% with a tolerance of 2, 3, and 4 occupants respectively. This study demonstrated that WiFi data coupled with stochastic machine learning system can provide a viable alternative to determine a building's occupancy profile.

1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen1 aZhu, Na uhttps://linkinghub.elsevier.com/retrieve/pii/S0360132318302464https://api.elsevier.com/content/article/PII:S0360132318302464?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0360132318302464?httpAccept=text/plain02004nas a2200157 4500008004100000245015700041210006900198260002500267520129500292100002001587700001601607700002101623700001901644700001801663856016501681 2018 eng d00aOpenBuildingControl: Modeling Feedback Control as a Step Towards Formal Design, Specification, Deployment and Verification of Building Control Sequences0 aOpenBuildingControl Modeling Feedback Control as a Step Towards  aChicago, ILc09/20183 a

This paper presents ongoing work to develop tools and a process that will allow building designers to instantiate control sequences, configure them for their project, assess their performance in closed loop building energy simulation, and then export these sequences (i) for the control provider to bid on the project and to implement the sequences through machine-to-machine translation, and (ii) for the commissioning agent to verify their correct implementation.

The paper reports on the following: (i) The specification of a Control Description Language, (ii) its use to implement a subset of the ASHRAE Guideline 36 sequences, released as part of the Modelica Buildings library, (iii) its use in annual closed-loop simulations of a variable air- volume flow system, and (iv) lessons learned regarding simulation of closed-loop control.

In our case study, the Guideline 36 sequences yield 30% lower annual site HYAC energy use, under comparable comfort, than sequences published earlier by ASHRAE. The 30% differences in annual HYAC energy consumption due to changes in the control sequences raises the question of whether the idealization of control sequences that is common practice in today's building energy simulation leads to trustworthy energy use predictions.

1 aWetter, Michael1 aHu, Jianjun1 aGrahovac, Milica1 aEubanks, Brent1 aHaves, Philip uhttps://www.ashrae.org/File%20Library/Conferences/Specialty%20Conferences/2018%20Building%20Performance%20Analysis%20Conference%20and%20SimBuild/Papers/C107.pdf01861nas a2200241 4500008004100000245010500041210006900146260001200215300000900227490000700236520110600243653001701349653002101366653002701387653002201414653001101436100001701447700001601464700001401480700001901494700001701513856008901530 2018 eng d00aPerformance-Based Evaluation of Courtyard Design in China’s Cold-Winter Hot-Summer Climate Regions0 aPerformanceBased Evaluation of Courtyard Design in China s ColdW c10/2018 a39500 v103 a

Evaluates the performance of the traditional courtyard design of the Jiangnan Museum, located in Jiangsu Province. In the evaluation, the spatial layout of courtyards is adjusted, the aspect ratio is changed, and an ecological buffer space is created. To model and evaluate the performance of the courtyard design, this study applied the Computational fluid dynamics (CFD) software, Parabolic Hyperbolic Or Elliptic Numerical Integration Code Series (PHOENICS), for wind environment simulation, and the EnergyPlus-based software, DesignBuilder, for energy simulation. Results show that a good combination of courtyard layout and aspect ratio can improve the use of natural ventilation by increasing free cooling during hot summers and reducing cold wind in winters. The results also show that ecological buffer areas of a courtyard can reduce cooling loads in summer by approximately 19.6% and heating loads in winter by approximately 22.3%. The study provides insights into the optimal design of a courtyard to maximize its benefit in regulating the microclimate during both winter and summer.

10aaspect ratio10acourtyard design10aecological buffer area10aecological effect10alayout1 aXu, Xiaodong1 aLuo, Fenlan1 aWang, Wei1 aHong, Tianzhen1 aFu, Xiuzhang uhttp://www.mdpi.com/2071-1050/10/11/3950http://www.mdpi.com/2071-1050/10/11/3950/pdf01432nas a2200121 4500008004100000245005900041210005900100520100900159100002001168700002001188700001801208856008401226 2018 eng d00aSimplifications for hydronic system models in Modelica0 aSimplifications for hydronic system models in Modelica3 a

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

1 aJorissen, Filip1 aWetter, Michael1 aHelsen, Lieve uhttps://simulationresearch.lbl.gov/publications/simplifications-hydronic-system02543nas 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/plain02453nas a2200253 4500008004100000245013600041210006900177260001200246300001200258490000800270520159800278653002101876653002301897653003201920653001601952653001901968653001401987653002502001653001802026100002302044700001902067700002902086856008402115 2017 eng d00aAnalysis of heating load diversity in German residential districts and implications for the application in district heating systems0 aAnalysis of heating load diversity in German residential distric c01/2017 a302-3130 v1393 a

In recent years, the application of district heating systems for the heat supply of residential districts has been increasing in Germany. Central supply systems can be very efficient due to diverse energy demand profiles which may lead to reduced installed equipment capacity. Load diversity in buildings has been investigated in former studies, especially for the electricity demand. However, little is known about the influence of single building characteristics (such as building envelope or hot water demand) on the overall heating peak load of a residential district. For measuring the diversity, the peak load ratio (PLR) index is used to represent the percentage reduction of peak load of a district system from a simple sum of individual peak loads of buildings. A total of 144 residential building load profiles have been created with the dynamic building simulation software IDA ICE for a theoretical analysis in which the PLR reaches 15%. Within this study, certain district features are identified which lead to higher diversity. Furthermore, these results are used in a district heating simulation model which confronts the possible advantage of reduced installed capacity with the practical disadvantage of heat distribution losses.  Likewise, the influence of load density and the district´s building structure can be analyzed. This study shows that especially in districts with high load density, which consist of newly constructed buildings with low supply temperature and high influence of the hot water demand, the advantages of load diversity can be exploited.

10adistrict heating10adomestic hot water10adynamic building simulation10aheat supply10aLoad diversity10apeak load10aresidential district10aspace heating1 aWeissmann, Claudia1 aHong, Tianzhen1 aGraubner, Carl-Alexander uhttps://simulationresearch.lbl.gov/publications/analysis-heating-load-diversity02405nas a2200217 4500008004100000245009900041210006900140520168500209653001901894653002301913653002301936653002101959653001701980653002701997100001202024700001902036700001202055700001502067700001702082856008802099 2017 eng d00aData Analytics and Optimization of an Ice-Based Energy Storage System for Commercial Buildings0 aData Analytics and Optimization of an IceBased Energy Storage Sy3 a

Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice–based TES system in a shopping mall, calculating the system’s performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system’s operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3% per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.

10aData Analytics10aenergy cost saving10aheuristic strategy10aMachine learning10aoptimization10aThermal energy storage1 aLuo, Na1 aHong, Tianzhen1 aLi, Hui1 aJia, Rouxi1 aWeng, Wenguo uhttps://simulationresearch.lbl.gov/publications/data-analytics-and-optimization-ice02289nas a2200217 4500008004100000245009600041210006900137490000800206520155700214100002601771700001801797700002701815700002201842700001801864700002301882700001701905700002901922700002001951700001801971856008201989 2017 eng d00aDynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems0 aDynamic equationbased thermohydraulic pipe model for district he0 v1513 a

Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.

1 avan der Heijde, Brahm1 aFuchs, Marcus1 aTugores, Carles, Ribas1 aSchweiger, Gerald1 aSartor, Kevin1 aBasciotti, Daniele1 aMuller, Dirk1 aNytsch-Geusen, Christoph1 aWetter, Michael1 aHelsen, Lieve uhttps://simulationresearch.lbl.gov/publications/dynamic-equation-based-thermo02095nas 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-pipe01392nas a2200133 4500008004100000245008600041210006900127260002700196520090500223653003501128100001601163700002001179856005901199 2017 eng d00aMPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings0 aMPCPy An OpenSource Software Platform for Model Predictive Contr aSan Franciscoc08/20173 a

Within the last decade, needs for building control systems that reduce cost, energy, or peak demand, and that facilitate building-grid integration, district-energy system optimization, and occupant interaction, while maintaining thermal comfort and indoor air quality, have come about.  Current PID and schedule-based control systems are not capable of fulfilling these needs, while Model Predictive Control (MPC) could.  Despite the critical role MPC-enabled buildings can play in future energy infrastructures, widespread adoption of MPC within the building industry has yet to occur.  To address barriers associated with system setup and configuration, this paper introduces an open-source software platform that emphasizes use of self-tuning adaptive models, usability by non-experts of MPC, and a flexible architecture that enables application across projects.

10aModel predictive control (MPC)1 aBlum, David1 aWetter, Michael uhttp://www.ibpsa.org/proceedings/BS2017/BS2017_351.pdf01862nas a2200133 4500008004100000245007800041210006900119260001200188520137400200100002501574700002001599700002201619856008701641 2016 eng d00aA Tale of Three District Energy Systems: Metrics and Future Opportunities0 aTale of Three District Energy Systems Metrics and Future Opportu c08/20173 a

Improving the sustainability of cities is crucial for meeting climate goals in the next several decades. One way this is being tackled is through innovation in district energy systems, which can take advantage of local resources and economies of scale to improve the performance of whole neighborhoods in ways infeasible for individual buildings. These systems vary in physical size, end use services, primary energy resources, and sophistication of control. They also vary enormously in their choice of optimization metrics while all under the umbrella-goal of improved sustainability.

This paper explores the implications of choice of metric on district energy systems using three case studies: Stanford University, the University of California at Merced, and the Richmond Bay campus of the University of California at Berkeley. They each have a centralized authority to implement large-scale projects quickly, while maintaining data records, which makes them relatively effective at achieving their respective goals. Comparing the systems using several common energy metrics reveals significant differences in relative system merit. Additionally, a novel bidirectional heating and cooling system is presented. This system is highly energy-efficient, and while more analysis is required, may be the basis of the next generation of district energy systems

1 aPass, Rebecca, Zarin1 aWetter, Michael1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/tale-three-district-energy-systems01769nas a2200121 4500008003900000245010800039210006900147520127800216100002001494700001801514700003101532856008401563 2015 d00aDesign choices for thermofluid flow components and systems that are exported as Functional Mockup Units0 aDesign choices for thermofluid flow components and systems that 3 a

This paper discusses design decisions for exporting Modelica thermofluid flow components as Functional Mockup Units. The purpose is to provide guidelines that will allow building energy simulation programs and HVAC equipment manufacturers to effectively use FMUs for modeling of HVAC components and systems. We provide an analysis for direct input-output dependencies of such components and discuss how these dependencies can lead to algebraic loops that are formed when connecting thermofluid flow components. Based on this analysis, we provide recommendations that increase the computing efficiency of such components and systems that are formed by connecting multiple components. We explain what code optimizations are lost when providing thermofluid flow components as FMUs rather than Modelica code. We present an implementation of a package for FMU export of such components, explain the rationale for selecting the connector variables of the FMUs and finally provide computing benchmarks for different design choices. It turns out that selecting temperature rather than specific enthalpy as input and output signals does not lead to a measurable increase in computing time, but selecting nine small FMUs rather than a large FMU increases computing time by 70%

1 aWetter, Michael1 aFuchs, Marcus1 aNouidui, Thierry, Stephane uhttps://simulationresearch.lbl.gov/publications/design-choices-thermofluid-flow01796nas a2200217 4500008003900000245010600039210006900145260001200214300001200226490000800238520106600246653002801312653001301340653002901353653002001382653001501402100002001417700001901437700003101456856009101487 2015 d00aEquation-based languages – A new paradigm for building energy modeling, simulation and optimization0 aEquationbased languages A new paradigm for building energy model c04/2016 a290-3000 v1173 a

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization.

We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. Exploiting the equation-based language led to 2200 times faster solution.

10aEquation-based modeling10amodelica10aMulti-physics simulation10aOptimal control10asmart grid1 aWetter, Michael1 aBonvini, Marco1 aNouidui, Thierry, Stephane uhttps://simulationresearch.lbl.gov/publications/equation-based-languages-%E2%80%93-new00387nas a2200121 4500008003900000245003100039210003100070100002000101700001600121700003100137700001800168856007900186 2015 d00aModelica Buildings Library0 aModelica Buildings Library1 aWetter, Michael1 aZuo, Wangda1 aNouidui, Thierry, Stephane1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/modelica-buildings-library01114nas a2200121 4500008003900000245009700039210006900136520064700205100002000852700002000872700001800892856008200910 2015 d00aSimulation Speed Analysis and Improvements of Modelica Models for Building Energy Simulation0 aSimulation Speed Analysis and Improvements of Modelica Models fo3 a

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

1 aJorissen, Filip1 aWetter, Michael1 aHelsen, Lieve uhttps://simulationresearch.lbl.gov/publications/simulation-speed-analysis-and03143nas a2200229 4500008003900000024002700039245009400066210006900160260004200229520232200271653003802593653003402631653000802665653003302673653002502706100001802731700002002749700002002769700002102789700002402810856007902834 2014 d aCEC‐500‐2015‐00100aDevelopment of Diagnostic and Measurement and Verification Tools for Commercial Buildings0 aDevelopment of Diagnostic and Measurement and Verification Tools bCalifornia Energy Commissionc09/20143 a

This research developed new measurement and verification tools and new automated fault detection and diagnosis tools, and deployed them in the Universal Translator. The Universal Translator is a tool, developed by Pacific Gas and Electric, that manages large sets of measured data from building control systems and enables off‐line analysis of building performance. There were four technical projects following the program administration tasks identified as Project 1:

  1. Program Administration
  2. Methods and Tools to Reduce the Cost of Measurement and Verification.
  3. Fault Detection and Diagnostics for Commercial Heating, Ventilating, and Air‐ Conditioning Systems.
  4. Test Procedures and Tools to Characterize Fan and Duct System Performance in Large Commercial Buildings.
  5. Universal Translator Development: Integration of Application Programming Interface.

Project 1 consisted of administrative tasks related to the project.

Project 2 addressed the need for less expensive measurement and verification tools to determine the costs and benefits of retrofits and retro‐commissioning at both the individual building level and the utility program level.

Project 3 extended previous work on fault detection and diagnosis to additional systems and subsystems, including dual duct heating, ventilating and air‐conditioning systems and fan‐coil terminal units.

Project 4 combined previous work on duct leakage and fan modeling to develop a performance assessment method for existing fan/duct systems that could also be used in the analysis of retrofit measures identified by the tools in Projects 2 and 3 using the EnergyPlus simulation program to help select the most cost‐effective package of improvements.

Some of the diagnostic methods and tools developed in projects 2 through 4 were incorporated in the Universal Translator via a new application programming interface that was specified, developed and tested in Project 5. Combined, these tools support analyses of energy savings produced by new construction commissioning, retro‐commissioning, improved routine operations and code compliance. The new application programming interface could also facilitate future development, testing and deployment of new diagnostic tools.

10aapplication programming interface10afault detection and diagnosis10aM&V10aMeasurement and verification10aUniversal Translator1 aHaves, Philip1 aWray, Craig, P.1 aJump, David, A.1 aVeronica, Daniel1 aFarley, Christopher uhttps://simulationresearch.lbl.gov/publications/development-diagnostic-and00559nas a2200181 4500008003900000245005300039210005300092100001900145700001400164700002500178700001200203700001400215700001400229700001500243700001500258700001700273856008700290 2014 d00aIntegrated Design for High Performance Buildings0 aIntegrated Design for High Performance Buildings1 aHong, Tianzhen1 aLi, Cheng1 aDiamond, Richard, C.1 aYan, Da1 aZhang, Qi1 aZhou, Xin1 aGuo, Siyue1 aSun, Kaiyu1 aWang, Jingyi uhttps://simulationresearch.lbl.gov/publications/integrated-design-high-performance02607nas a2200169 4500008003900000245006200039210006000101520204900161100001902210700001802229700001902247700001702266700002302283700002002306700002102326856009002347 2014 d00aA New Model to Simulate Energy Performance of VRF Systems0 aNew Model to Simulate Energy Performance of VRF Systems3 a

This paper presents a new model to simulate energy performance of variable refrigerant flow (VRF) systems in heat pump operation mode (either cooling or heating is provided but not simultaneously). The main improvement of the new model is the introduction of the evaporating and condensing temperature in the indoor and outdoor unit capacity modifier functions. The independent variables in the capacity modifier functions of the existing VRF model in EnergyPlus are mainly room wet-bulb temperature and outdoor dry-bulb temperature in cooling mode and room dry-bulb temperature and outdoor wet-bulb temperature in heating mode. The new approach allows compliance with different specifications of each indoor unit so that the modeling accuracy is improved. The new VRF model was implemented in a custom version of EnergyPlus 7.2. This paper first describes the algorithm for the new VRF model, which is then used to simulate the energy performance of a VRF system in a Prototype House in California that complies with the requirements of Title 24 – the California Building Energy Efficiency Standards. The VRF system performance is then compared with three other types of HVAC systems: the Title 24-2005 Baseline system, the traditional High Efficiency system, and the EnergyStar Heat Pump system in three typical California climates: Sunnyvale, Pasadena and Fresno. Calculated energy savings from the VRF systems are significant. The HVAC site energy savings range from 51 to 85%, while the TDV (Time Dependent Valuation) energy savings range from 31 to 66% compared to the Title 24 Baseline Systems across the three climates. The largest energy savings are in Fresno climate followed by Sunnyvale and Pasadena. The paper discusses various characteristics of the VRF systems contributing to the energy savings. It should be noted that these savings are calculated using the Title 24 prototype House D under standard operating conditions. Actual performance of the VRF systems for real houses under real operating conditions will vary.

1 aHong, Tianzhen1 aPang, Xiufeng1 aSchetrit, Oren1 aWang, Liping1 aKasahara, Shinichi1 aYura, Yoshinori1 aHinokuma, Ryohei uhttps://simulationresearch.lbl.gov/publications/new-model-simulate-energy-performance02500nas a2200253 4500008003900000022003900039245010600078210006900184260003900253300001200292490000600304520167200310653003701982653002702019653001502046653000902061653001302070653001502083100001602098700001902114700001202133700001702145856008402162 2013 d aPrint: 1996-3599; Online 1996-874400aA Detailed Loads Comparison of Three Building Energy Modeling Programs: EnergyPlus, DeST and DOE-2.1E0 aDetailed Loads Comparison of Three Building Energy Modeling Prog bTsinghua University Pressc09/2013 a323-3350 v63 a

Building energy simulation is widely used to help design energy efficient building envelopes and HVAC systems, develop and demonstrate compliance of building energy codes, and implement building energy rating programs. However, large discrepancies exist between simulation results from different building energy modeling programs (BEMPs). This leads many users and stakeholders to lack confidence in the results from BEMPs and building simulation methods. This paper compared the building thermal load modeling capabilities and simulation results of three BEMPs: EnergyPlus, DeST and DOE-2.1E. Test cases, based upon the ASHRAE Standard 140 tests, were designed to isolate and evaluate the key influencing factors responsible for the discrepancies in results between EnergyPlus and DeST. This included the load algorithms and some of the default input parameters. It was concluded that there is little difference between the results from EnergyPlus and DeST if the input values are the same or equivalent despite there being many discrepancies between the heat balance algorithms. DOE-2.1E can produce large errors for cases when adjacent zones have very different conditions, or if a zone is conditioned part-time while adjacent zones are unconditioned. This was due to the lack of a strict zonal heat balance routine in DOE-2.1E, and the steady state handling of heat flow through interior walls and partitions. This comparison study did not produce another test suite, but rather a methodology to design tests that can be used to identify and isolate key influencing factors that drive the building thermal loads, and a process with which to carry them out.

10abuilding energy modeling program10abuilding thermal loads10acomparison10adest10aDOE-2.1E10aenergyplus1 aZhu, Dandan1 aHong, Tianzhen1 aYan, Da1 aWang, Chuang uhttps://simulationresearch.lbl.gov/publications/detailed-loads-comparison-three01616nas a2200133 4500008003900000245006700039210006500106260003000171520113400201100003101335700002001366700001601386856008001402 2013 d00aFunctional Mock-Up Unit Import in EnergyPlus For Co-Simulation0 aFunctional MockUp Unit Import in EnergyPlus For CoSimulation aChambery, Francec08/20133 a

This paper describes how to use the recently implemented Functional Mock-up Unit (FMU) for co-simulation import interface in EnergyPlus to link EnergyPlus with simulation tools packaged as FMUs. The interface complies with the Functional Mock-up Interface (FMI) for co-simulation standard version 1.0, which is an open standard designed to enable links between different simulation tools that are packaged as FMUs. This article starts with an introduction of the FMI and FMU concepts. We then discuss the implementation of the FMU import interface in EnergyPlus. After that, we present two use cases. The first use case is to model a HVAC system in Modelica, export it as an FMU, and link it to a room model in EnergyPlus. The second use case is an extension of the first case where a shading controller is modeled in Modelica, exported as an FMU, and used in the EnergyPlus room model to control the shading device of one of its windows. In both cases, the FMUs are imported into EnergyPlus which models the building envelope and manages the data-exchange between the envelope and the systems in the FMUs during run-time.

1 aNouidui, Thierry, Stephane1 aWetter, Michael1 aZuo, Wangda uhttps://simulationresearch.lbl.gov/publications/functional-mock-unit-import00784nas a2200241 4500008004100000245006200041210006200103260003200165653004300197653003700240653002400277653001500301653000900316653001000325653001500335653003000350653000900380100001600389700001900405700001200424700001700436856008900453 2012 eng d00aComparative research in building energy modeling programs0 aComparative research in building energy modeling programs aChina (in Chinese)c06/201110aadvanced building software: energyplus10abuilding energy modeling program10abuilding simulation10acomparison10adest10adoe-210aenergyplus10asimulation research group10atest1 aZhu, Dandan1 aHong, Tianzhen1 aYan, Da1 aWang, Chuang uhttps://simulationresearch.lbl.gov/publications/comparative-research-building-energy00448nas a2200121 4500008003900000245006800039210006700107260001200174100001600186700001900202700001700221856008800238 2012 d00aComparison of Building Energy Modeling Programs: Building Loads0 aComparison of Building Energy Modeling Programs Building Loads c06/20121 aZhu, Dandan1 aHong, Tianzhen1 aWang, Chuang uhttps://simulationresearch.lbl.gov/publications/comparison-building-energy-modeling01519nas a2200121 4500008003900000245011500039210006900154520103400223100001701257700001801274700002001292856008501312 2012 d00aThe Energy Saving Potential of Membrane-Based Enthalpy Recovery in Vav Systems for Commercial Office Buildings0 aEnergy Saving Potential of MembraneBased Enthalpy Recovery in Va3 a

A design tool to evaluate the heat and mass transfer effectiveness and pressure drop of a membrane-based enthalpy exchanger was developed and then used to optimize the configuration of an enthalpy exchanger for minimum pressure drop and maximum heat recovery effectiveness. Simulation was used in a parametric study to investigate the energy saving potential of the enthalpy recovery system. The case without energy recovery and air side economizer was used as a baseline. Two comparison cases for the implementation of enthalpy recovery with and without air side economizer were simulated in EnergyPlus. A case using a desiccant wheel for energy recovery was also investigated for comparison purposes. The simulation results show significant energy saving benefits from applying a low pressure drop, high effectiveness enthalpy exchanger in two US cities representing a range of humid climates. The sensitivity of the energy savings potential to pressure drop and heat and mass transfer effectivenesses is also presented.

1 aWang, Liping1 aHaves, Philip1 aBreshears, John uhttps://simulationresearch.lbl.gov/publications/energy-saving-potential-membrane00520nas a2200121 4500008004100000245011400041210006900155260003200224100001700256700001800273700002000291856008700311 2012 eng d00aThe Energy Saving Potential of Membrane-Based Enthalpy Recovery in VAV System for Commercial Office Buildings0 aEnergy Saving Potential of MembraneBased Enthalpy Recovery in VA aMadison, Wisconsinc08/20121 aWang, Liping1 aHaves, Philip1 aBreshears, John uhttps://simulationresearch.lbl.gov/publications/energy-saving-potential-membrane-001987nas a2200241 4500008004100000022001400041245008500055210006900140260001200209300001200221490000700233520120100240653003901441653002501480653002001505653001501525653003401540100001801574700002001592700002601612700001801638856008901656 2012 eng d a0360-132300aA framework for simulation-based real-time whole building performance assessment0 aframework for simulationbased realtime whole building performanc c08/2012 a100-1080 v543 a

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures. One approach to addressing this problems is to compare the predictions of an energy simulation model of the building to the measured performance and analyze significant differences to infer the presence and location of faults. This paper presents a framework that allows a comparison of building actual performance and expected performance in real time. The realization of the framework utilized the EnergyPlus, the Building Controls Virtual Test Bed (BCVTB) and the Energy Management and Control System (EMCS) was developed. An EnergyPlus model that represents expected performance of a building runs in real time and reports the predicted building performance at each time step. The BCVTB is used as the software platform to acquire relevant inputs from the EMCS through a BACnet interface and send them to the EnergyPlus and to a database for archiving. A proof-of-concept demonstration is also presented.

10abuilding controls virtual test bed10abuilding performance10aenergy modeling10aenergyplus10areal-time building simulation1 aPang, Xiufeng1 aWetter, Michael1 aBhattacharya, Prajesh1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/framework-simulation-based-real-time01431nas a2200133 4500008003900000245005600039210005300095260001200148520099100160100001701151700001801168700002101186856009001207 2012 d00aAn Improved Simple Chilled Water Cooling Coil Model0 aImproved Simple Chilled Water Cooling Coil Model c08/20123 a

The accurate prediction of cooling and dehumidification coil performance is important in model-based fault detection and in the prediction of HVAC system energy consumption for support of both design and operations. It is frequently desirable to use a simple cooling coil model that does not require detailed specification of coil geometry and material properties. The approach adopted is to match the overall UA of the coil to the rating conditions and to estimate the air-side and water-side components of the UA using correlations developed by Holmes (1982). This approach requires some geometrical information about the coil and the paper investigates the sensitivity of the overall performance prediction to uncertainties in this information, including assuming a fixed ratio of air-side to water-side UA at the rating condition. Finally, simulation results from different coil models are compared, and experimental data are used to validate the improved cooling coil model.

1 aWang, Liping1 aHaves, Philip1 aBuhl, Walter, F. uhttps://simulationresearch.lbl.gov/publications/improved-simple-chilled-water-cooling02060nas a2200277 4500008003900000245009300039210006900132260001200201520117000213653001701383653001801400653001501418653003601433653002301469653001501492100001901507700002301526700001801549700003101567700001701598700001701615700002501632700001701657700002001674856008801694 2012 d00aMonitoring-based HVAC Commissioning of an Existing Office Building for Energy Efficiency0 aMonitoringbased HVAC Commissioning of an Existing Office Buildin c10/20123 a

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

10abenchmarking10acommissioning10aenergyplus10afault detection and diagnostics10afunctional testing10atrend data1 aEarni, Shankar1 aWoodworth, Spencer1 aPang, Xiufeng1 aHernandez-Maldonado, Jorge1 aYin, Rongxin1 aWang, Liping1 aGreenberg, Steve, E.1 aFiegel, John1 aRubalcava, Alma uhttps://simulationresearch.lbl.gov/publications/monitoring-based-hvac-commissioning00703nas a2200205 4500008003900000022001300039245012000052210006900172260001200241300001400253490000700267653002400274653001500298653002500313653001800338100001700356700002100373700001800394856008500412 2012 d a0378778800aUncertainties in energy consumption introduced by building operations and weather for a medium-size office building0 aUncertainties in energy consumption introduced by building opera c10/2012 a152 - 1580 v5310aBuilding Operations10aenergyplus10aMonte Carlo Analysis10aUncertainties1 aWang, Liping1 aMathew, Paul, A.1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/uncertainties-energy-consumption01281nas a2200145 4500008004100000245008300041210006900124260002900193520073500222100003100957700002100988700001601009700002001025856009001045 2012 eng d00aValidation and Application of the Room Model of the Modelica Buildings Library0 aValidation and Application of the Room Model of the Modelica Bui aMunich, Germanyc09/20123 a

The Modelica Buildings library contains a package with a model for a thermal zone that computes heat transfer through the building envelope and within a room. It considers various heat transfer phenomena of a room, including conduction, convection, short-wave and long-wave radiation. The first part of this paper describes the physical phenomena considered in the room model. The second part validates the room model by using a standard test suite provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). The third part focuses on an application where the room model is used for simulation-based controls of a window shading device to reduce building energy consumption.

1 aNouidui, Thierry, Stephane1 aPhalak, Kaustubh1 aZuo, Wangda1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/validation-and-application-room-model01198nas a2200133 4500008003900000245006900039210006900108260001200177520072300189100003100912700002000943700001600963856008500979 2012 d00aValidation of the Window Model of the Modelica Buildings Library0 aValidation of the Window Model of the Modelica Buildings Library c07/20123 a

This paper describes the validation of the window model of the free open-source Modelica Buildings library. This paper starts by describing the physical modeling assumptions of the window model. The window model can be used to calculate the thermal and angular properties of glazing systems. It can also be used for steady-state simulation of heat transfer mechanism in glazing systems. We present simulation results obtained by comparing the window model with WINDOW 6 the well established simulation tool for steady-state heat transfer in glazing systems. We also present results obtained by comparing the window model with measurements carried out in a test cell at the Lawrence Berkeley National Laboratory.

1 aNouidui, Thierry, Stephane1 aWetter, Michael1 aZuo, Wangda uhttps://simulationresearch.lbl.gov/publications/validation-window-model-modelica00476nas a2200109 4500008004100000245011200041210006900153260001900222100001600241700002000257856008900277 2011 eng d00aAdvanced simulations of building energy and control systems with an example of chilled water plant modeling0 aAdvanced simulations of building energy and control systems with aNanjing, China1 aZuo, Wangda1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/advanced-simulations-building-energy00620nas a2200169 4500008004100000245008700041210006900128260003100197100002300228700001800251700001500269700002100284700002100305700002100326700002000347856008300367 2011 eng d00aAn Assessment of the use of Building Energy Performance Simulation in Early Design0 aAssessment of the use of Building Energy Performance Simulation  aSydney, Australiac11/20111 aBazjanac, Vladimir1 aMaile, Tobias1 aRose, Cody1 aO'Donnell, James1 aMrazovic, Natasa1 aMorrissey, Elmer1 aWelle, Benjamin uhttps://simulationresearch.lbl.gov/publications/assessment-use-building-energy01404nas a2200169 4500008003900000245008200039210006900121260001200190520081400202100001801016700002601034700003101060700002001091700001701111700001801128856008801146 2011 d00aBacNet and Analog/Digital Interfaces of the Building Controls Virtual Testbed0 aBacNet and AnalogDigital Interfaces of the Building Controls Vir c11/20113 a

This paper gives an overview of recent developments in the Building Controls Virtual Test Bed (BCVTB), a framework for co-simulation and hardware-in-the-loop.

First, a general overview of the BCVTB is presented. Second, we describe the BACnet interface, a link which has been implemented to couple BACnet devices to the BCVTB. We present a case study where the interface was used to couple a whole building simulation program to a building control system to assess in real-time the performance of a real building. Third, we present the ADInterfaceMCC, an analog/digital interface that allows a USB-based analog/digital converter to be linked to the BCVTB. In a case study, we show how the link was used to couple the analog/digital converter to a building simulation model for local loop control.

1 aHaves, Philip1 aBhattacharya, Prajesh1 aNouidui, Thierry, Stephane1 aWetter, Michael1 aLi, Zhengwei1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/bacnet-and-analogdigital-interfaces00629nas a2200169 4500008004100000245008300041210006900124260003100193300001500224100003100239700002000270700001700290700001800307700002600325700001800351856009000369 2011 eng d00aBACnet and Analog/Digital Interfaces of the Building Controls Virtual Test Bed0 aBACnet and AnalogDigital Interfaces of the Building Controls Vir aSydney, Australiac11/2011 ap. 294-3011 aNouidui, Thierry, Stephane1 aWetter, Michael1 aLi, Zhengwei1 aPang, Xiufeng1 aBhattacharya, Prajesh1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/bacnet-and-analogdigital-interfaces-000666nas a2200229 4500008004100000245004000041210003800081260001800119653002400137653001500161653000900176653002000185653001500205653002400220653003000244653001500274100001600289700001700305700001200322700001900334856008300353 2011 eng d00aA Comparison of DeST and EnergyPlus0 aComparison of DeST and EnergyPlus aBeijingc201110abuilding simulation10acomparison10adest10aenergy modeling10aenergyplus10asimulation research10asimulation research group10atest cases1 aZhu, Dandan1 aWang, Chuang1 aYan, Da1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/comparison-dest-and-energyplus02106nas a2200169 4500008004100000245010100041210006900142260001200211490000600223520151300229653002401742653001801766653002401784653002201808100002001830856008601850 2011 eng d00aCo-simulation of building energy and control systems with the Building Controls Virtual Test Bed0 aCosimulation of building energy and control systems with the Bui c11/20100 v33 a

This article describes the implementation of the Building Controls Virtual Test Bed (BCVTB). The BCVTB is a software environment that allows connecting different simulation programs to exchange data during the time integration, and that allows conducting hardware in the loop simulation. The software architecture is a modular design based on Ptolemy II, a 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. The BCVTB provides additions to Ptolemy II that allow the run-time coupling of different simulation programs for data exchange, including EnergyPlus, MATLAB, Simulink and the Modelica modelling and simulation environment Dymola. The additions also allow executing system commands, such as a script that executes a Radiance simulation. In this article, the software architecture is presented and the mathematical model used to implement the co-simulation is discussed. The simulation program interface that the BCVTB provides is explained. The article concludes by presenting applications in which different state of the art simulation programs are linked for run-time data exchange. This link allows the use of the simulation program that is best suited for the particular problem to model building heat transfer, HVAC system dynamics and control algorithms, and to compute a solution to the coupled problem using co-simulation.

10abuilding simulation10aco-simulation10aintegrated analysis10amodular modelling1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/co-simulation-building-energy-and00768nas a2200241 4500008004100000245005700041210005700098260002300155653004300178653002400221653001500245653001100260653001200271653001300283653001800296653003000314100002200344700001800366700001800384700001700402700001900419856008800438 2011 eng d00aModeling and simulation of HVAC faults in EnergyPlus0 aModeling and simulation of HVAC faults in EnergyPlus aAustraliac11/201110aadvanced building software: energyplus10abuilding simulation10aenergyplus10afaults10afouling10amodeling10asensor offset10asimulation research group1 aBasarkar, Mangesh1 aHaves, Philip1 aPang, Xiufeng1 aWang, Liping1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-and-simulation-hvac-faults00466nas a2200133 4500008003900000245005800039210005800097260001200155100002200167700001800189700001700207700001900224856008900243 2011 d00aModeling and simulation of HVAC Results in EnergyPlus0 aModeling and simulation of HVAC Results in EnergyPlus c11/20111 aBasarkar, Mangesh1 aPang, Xiufeng1 aWang, Liping1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-and-simulation-hvac-results00521nas a2200133 4500008004100000245007500041210006900116260003100185300001400216100002000230700001600250700003100266856009000297 2011 eng d00aModeling of Heat Transfer in Rooms in the Modelica "Buildings" Library0 aModeling of Heat Transfer in Rooms in the Modelica Buildings Lib aSydney, Australiac11/2011 a1096-11031 aWetter, Michael1 aZuo, Wangda1 aNouidui, Thierry, Stephane uhttps://simulationresearch.lbl.gov/publications/modeling-heat-transfer-rooms-modelica01677nas a2200181 4500008004100000245010100041210006900142260003100211300001700242520102700259100001801286700002601304700001901330700001801349700002001367700001901387856008901406 2011 eng d00aReal-time Building Energy Simulation using EnergyPlus and the Building Controls Virtual Test Bed0 aRealtime Building Energy Simulation using EnergyPlus and the Bui aSydney, Australiac11/2011 ap. 2890-28963 a

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures (Haves et al., 2001, Lee et al., 2007). To address this problem, the paper presents a simulation-based whole building performance monitoring tool that allows a comparison of building actual performance and expected performance in real time. The tool continuously acquires relevant building model input variables from existing Energy Management and Control System (EMCS). It then reports expected energy consumption as simulated of EnergyPlus. The Building Control Virtual Test Bed (BCVTB) is used as the software platform to provide data linkage between the EMCS, an EnergyPlus model, and a database. This paper describes the integrated real-time simulation environment. A proof-of-concept demonstration is also presented in the paper.

1 aPang, Xiufeng1 aBhattacharya, Prajesh1 aO'Neill, Zheng1 aHaves, Philip1 aWetter, Michael1 aBailey, Trevor uhttps://simulationresearch.lbl.gov/publications/real-time-building-energy-simulation01476nas a2200157 4500008004100000245009800041210006900139260002100208520088700229653002801116653001201144100002001156700001601176700003101192856009501223 2011 eng d00aRecent developments of the Modelica Buildings library for building energy and control systems0 aRecent developments of the Modelica Buildings library for buildi aDresden, Germany3 a

At the Modelica 2009 conference, we introduced the Buildings library, a freely available Modelica library for building energy and control systems [16]. This paper reports the updates of the library and presents example applications for a range of heating, ventilation and air conditioning (HVAC) systems. Over the past two years, the library has been further developed. The number of HVAC components models has been doubled and various components have been revised to increase numerical robustness. The paper starts with an overview of the library architecture and a description of the main packages. To demonstrate the features of the Buildings library, applications that include multizone airow simulation as well as supervisory and local loop control of a variable air volume (VAV) system are briey described. The paper closes with a discussion of the current development.

10abuilding energy systems10aheating1 aWetter, Michael1 aZuo, Wangda1 aNouidui, Thierry, Stephane uhttps://www.modelica.org/events/modelica2011/Proceedings/pages/papers/12_1_ID_113_a_fv.pdf02428nas a2200217 4500008004100000245013000041210006900171300001200240490000600252520155800258653001401816653003301830653003301863653009601896653001701992653004902009100002802058700002002086700002102106856008302127 2010 eng d00aA comparison of global optimization algorithms with standard benchmark functions and real-world applications using EnergyPlus0 acomparison of global optimization algorithms with standard bench a103-1200 v33 a

There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimize the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified controls sequence of the building energy system that does not represent actual practice, further reinforcing their utility.

10aalgorithm10aapplication using energyplus10abuilding energy minimization10acovariance matrix adaptation evolution strategy algorithm and hybrid differential evolution10aoptimization10aparticle swarm optimization and hooke-jeeves1 aKämpf, Jérôme, Henri1 aWetter, Michael1 aRobinson, Darren uhttps://simulationresearch.lbl.gov/publications/comparison-global-optimization01832nas a2200157 4500008004100000245015100041210006900192260001200261300001200273490000700285520123900292100001801531700001601549700002001565856008901585 2010 eng d00aCo-simulation for performance prediction of integrated building and HVAC systems - An analysis of solution characteristics using a two-body system0 aCosimulation for performance prediction of integrated building a c08/2010 a957-9700 v183 a

Integrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC) systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation to integrate different BPS tools. Co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration.

This article analyzes how co-simulation influences consistency, stability and accuracy of the numerical approximation to the solution. Consistency and zero-stability are studied for a general class of the problem, while a detailed consistency and absolute stability analysis is given for a simple two-body problem. Since the accuracy of the numerical approximation to the solution is reduced in co-simulation, the article concludes by discussing ways for how to improve accuracy.

1 aTrcka, Marija1 aHensen, Jan1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/co-simulation-performance-prediction02003nas a2200193 4500008004100000022001800041245011000059210006900169260004100238520131100279100002201590700001801612700002001630700002001650700002401670700001501694700001801709856008201727 2010 eng d a0-918249-60-000aDevelopment and Testing of Model Predictive Control for a Campus Chilled Water Plant with Thermal Storage0 aDevelopment and Testing of Model Predictive Control for a Campus aAsilomar, California, USAbOmnipress3 a

A Model Predictive Control (MPC) implementation was developed for a university campus chilled water plant. The plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. A detailed model of the chilled water plant and simplified models of the campus buildings were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers' performance data, and calibrated using measured data collected and archived by the control system. A dynamic model of the chilled water storage tank was also developed and calibrated. A semi-empirical model was developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a MPC algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The paper describes the development and testing of the MPC implementation and discusses lessons learned and next steps in further research.

1 aCoffey, Brian, E.1 aHaves, Philip1 aWetter, Michael1 aHencey, Brandon1 aBorrelli, Francesco1 aMa, Yudong1 aBengea, Sorin uhttps://simulationresearch.lbl.gov/publications/development-and-testing-model00555nas a2200157 4500008004100000245009400041210006900135300001200204490000800216100001900224700001900243700001500262700001600277700001800293856008600311 2010 eng d00aImpacts of Static Pressure Reset on VAV System Air Leakage, Fan Power, and Thermal Energy0 aImpacts of Static Pressure Reset on VAV System Air Leakage Fan P a428-4360 v1161 aLiu, Mingsheng1 aFeng, Jingjuan1 aWang, Zhan1 aZheng, Keke1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/impacts-static-pressure-reset-vav00462nas a2200145 4500008004100000245004900041210004900090260001200139300001400151490000700165100001700172700002000189700001900209856008800228 2009 eng d00aCase study of zero energy house design in UK0 aCase study of zero energy house design in UK c11/2009 a1215-12220 v411 aWang, Liping1 aGwilliam, Julie1 aJones, Phillip uhttps://simulationresearch.lbl.gov/publications/case-study-zero-energy-house-design02303nas a2200169 4500008004100000245008100041210006900122300001200191490000800203520174400211100001401955700001901969700001801988700001502006700002202021856009002043 2009 eng d00aCCLEP Reduces Energy Consumption by More than 50% for a Luxury Shopping Mall0 aCCLEP Reduces Energy Consumption by More than 50 for a Luxury Sh a492-5010 v1153 a

The Continuous Commissioning Leading Project (CCLEP) process is an ongoing process to apply system optimization theory and advanced technologies to commercial retrofit projects. It was developed by Liu et al (2006) through a U.S. Department of Energy grant to the University of Nebraska and the Omaha Public Power District (OPPD) for continuous commissioning applications in commercial retrofit projects. The CCLEP process, procedures and seven case study results have already been presented (Liu et al 2006).

CCLEP was applied to a luxury shopping mall and office building. The case study building has ten single fan dual-duct VAV AHUs, 123 dual-duct pneumatic controller pressure independent terminal boxes, and a central heating and cooling plant. Major retrofit efforts include upgrading pneumatic to DDC controls for all AHUs, installing main hot deck dampers, replacing the boiler, installing VFD on fans and pumps, and installing Fan Airflow Stations (FAS) and Pump Waterflow Stations (PWS). This paper presents the optimal control strategies, which include main hot deck damper control, supply fan control integrated with FAS, return fan control, optimal control for terminal boxes, chilled water temperature and chilled water pump speed control, hot water temperature and hot water pump control. The measured hourly utility data after CCLEP show that annual HVAC electricity consumption is reduced by 56% and gas use is reduced by 36%.

This paper demonstrates the energy savings and system performance improvement through retrofits and optimal system control. This paper will present the case study building information, CCLEP major retrofits, CCLEP optimal control strategies, CCLEP results and conclusions

1 aWu, Lixia1 aLiu, Mingsheng1 aPang, Xiufeng1 aWang, Gang1 aLewis, Thomas, G. uhttps://simulationresearch.lbl.gov/publications/cclep-reduces-energy-consumption-more01687nas a2200193 4500008004100000245006900041210006800110300001200178490000600190520103000196653003601226653001801262653002001280653005601300100001801356700001601374700002001390856008301410 2009 eng d00aCo-simulation of innovative integrated HVAC systems in buildings0 aCosimulation of innovative integrated HVAC systems in buildings a209-2300 v23 a

Integrated performance simulation of buildings HVAC systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation, as an integrated approach to simulation. This article elaborates on issues important for co-simulation realization and discusses multiple possibilities to justify the particular approach implemented in the here described co-simulation prototype. The prototype is validated with the results obtained from the traditional simulation approach. It is further used in a proof-of-concept case study to demonstrate the applicability of the method and to highlight its benefits. Stability and accuracy of different coupling strategies are analyzed to give a guideline for the required coupling time step.

10abuilding performance simulation10aco-simulation10ahvac simulation10ainnovative building system modelling and simulation1 aTrcka, Marija1 aHensen, Jan1 aWetter, Michael uhttp://www.informaworld.com/smpp/section?content=a913244253&fulltext=71324092800575nas a2200133 4500008004100000245017900041210006900220260001200289300001100301490000700312100001700319700002100336856008400357 2009 eng d00aCoupled simulations for naturally ventilated rooms between building simulation (BS) and computational fluid dynamics (CFD) for better prediction of indoor thermal environment0 aCoupled simulations for naturally ventilated rooms between build c01/2009 a95-1120 v441 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/coupled-simulations-naturally-001686nas a2200133 4500008004100000050001500041245005900056210005700115300001200172490000800184520125400192100002001446856008601466 2009 eng d aLBNL-2077E00aGeneric Optimization Program User Manual Version 3.0.00 aGeneric Optimization Program User Manual Version 300 a380-3910 v1133 a

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

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

1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/generic-optimization-program-user01911nas a2200145 4500008004100000245011700041210006900158260003100227300001200258520137900270100001801649700002001667700001601687856006201703 2009 eng d00aAn implementation of co-simulation for performance prediction of innovative integrated HVAC systems in buildings0 aimplementation of cosimulation for performance prediction of inn aGlasgow, Scotlandc07/2009 a724-7313 aIntegrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC)systems can help reducing energy consumption and increasing level of occupant comfort. However, no singe building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to accommodate the ever-increasing complexity and rapid innovations in building and system technologies. One way to alleviate this problem is to use co-simulation. The co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration. This paper elaborates on issues important for co-simulation realization and discusses multiple possibilities to justify the particular approach implemented in a co-simulation prototype. The prototype is verified and validated against the results obtained from the traditional simulation approach. It is further used in a case study for the proof-of-concept, to demonstrate the applicability of the method and to highlight its benefits. Stability and accuracy of different coupling strategies are analyzed to give a guideline for the required coupling frequency. The paper concludes by defining requirements and recommendations for generic co-simulation implementations.1 aTrcka, Marija1 aWetter, Michael1 aHensen, Jan uhttp://www.ibpsa.org/proceedings/BS2009/BS09_0724_731.pdf01678nas a2200133 4500008004100000245008400041210006900125250000700194260002500201490000700226520121900233100002001452856007201472 2009 eng d00aModelica Library for Building Heating, Ventilation and Air-Conditioning Systems0 aModelica Library for Building Heating Ventilation and AirConditi a44 aComo, Italyc09/20090 v433 a

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

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

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

10amodelica1 aWetter, Michael uhttp://www.ibpsa.org/proceedings/BS2009/BS09_0652_659.pdf01955nas a2200133 4500008004100000245011800041210006900159300001200228490000600240520145900246653001301705100002001718856008301738 2009 eng d00aModelica-based Modeling and Simulation to Support Research and Development in Building Energy and Control Systems0 aModelicabased Modeling and Simulation to Support Research and De a143-1610 v23 aTraditional building simulation programs possess attributes that make them difficult to use for the design and analysis of building energy and control systems and for the support of model-based research and development of systems that may not already be implemented in these programs. This paper presents characteristic features of such applications, and it shows how equation-based object-oriented modeling can meet requirements that arise in such applications. Next, the implementation of an open-source component model library for building energy systems is presented. The library has been developed using the equation-based object-oriented Modelica modeling language. Technical challenges of modeling and simulating such systems are discussed. Research needs are presented to make this technology accessible to user groups that have more stringent requirements with respect to the numerical robustness of simulation than a research community may have. Two examples are presented in which models from the here described library were used. The first example describes the design of a controller for a nonlinear model of a heating coil using model reduction and frequency domain analysis. The second example describes the tuning of control parameters for a static pressure reset controller of a variable air volume flow system. The tuning has been done by solving a non-convex optimization problem that minimizes fan energy subject to state constraints.10amodelica1 aWetter, Michael uhttp://www.informaworld.com/smpp/section?content=a911401852&fulltext=71324092802217nas a2200217 4500008004100000020002200041245006700063210006400130250000700194260006500201490000700266520151600273653001301789100002101802700002301823700001801846700002001864700002301884700002001907856007201927 2009 eng d a978-91-7393-513-500aStandardization of thermo-fluid modeling in Modelica.Fluid 1.00 aStandardization of thermofluid modeling in ModelicaFluid 10 a13 aComo, ItalybLinköping University Electronic Pressc09/20090 v433 a

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

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

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

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

10amodelica1 aFranke, Rüdiger1 aCasella, Francesco1 aOtter, Martin1 aProelss, Katrin1 aSielemann, Michael1 aWetter, Michael uhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=1300537nas a2200145 4500008004100000022001500041245012400056210006900180260001500249100002100264700001700285700001900302700002100321856004900342 2009 eng d aLBNL-2819E00aTechnical Assistance to Beichuan Reconstruction: Creating and Designing Low- to Zero-carbon Communities in New Beichuan0 aTechnical Assistance to Beichuan Reconstruction Creating and Des bLBNLc20091 aXu, Tengfang, T.1 aWang, Chuang1 aHong, Tianzhen1 aLevine, Mark, D. uhttp://www.escholarship.org/uc/item/0vv4m1gb02680nas a2200133 4500008004100000245008800041210006900129260003600198520216900234100001802403700002202421700002002443856008302463 2008 eng d00aBenchmarking and Equipment and Controls Assessment for a ‘Big Box’ Retail Chain0 aBenchmarking and Equipment and Controls Assessment for a Big Box aAsilomar, California, USAc20083 a

The paper describes work to enable improved energy performance of existing and new retail stores belonging to a national chain and thereby also identify measures and tools that would improve the performance of ‘big box' stores generally. A detailed energy simulation model of a standard store design was developed and used to:

The core enabling task of the project was to develop an energy model of the current standard design using the EnergyPlus simulation program. For the purpose of verification of the model against actual utility bills, the model was reconfigured to represent twelve existing stores (seven relatively new stores and five older stores) in different US climates and simulations were performed using weather data obtained from the National Weather Service. The results of this exercise, which showed generally good agreement between predicted and measured total energy use, suggest that dynamic benchmarking based on energy simulation would be an effective tool for identifying operational problems that affect whole building energy use. The models of the seven newer stores were then configured with manufacturers' performance data for the equipment specified in the current design and used to assess the energy and cost benefits of increasing the efficiency of selected HVAC, lighting and envelope components. The greatest potential for cost-effective energy savings appears to be a substantial increase in the efficiency of the blowers in the roof top units and improvements in the efficiency of the lighting. The energy benefits of economizers on the roof-top units were analyzed and found to be very sensitive to the operation of the exhaust fans used to control building pressurization.

1 aHaves, Philip1 aCoffey, Brian, E.1 aWilliams, Scott uhttps://simulationresearch.lbl.gov/publications/benchmarking-and-equipment-and01364nas a2200229 4500008004100000245006400041210006400105260000900169300001200178490000600190520067400196653002200870653001000892653001500902653002300917100001900940700002100959700001800980700002700998700002001025856008901045 2008 eng d00aComparing computer run time of building simulation programs0 aComparing computer run time of building simulation programs c2008 a210-2130 v13 a

This paper presents an approach to comparing computer run time of building simulation programs. The computing run time of a simulation program depends on several key factors, including the calculation algorithm and modeling capabilities of the program, the run period, the simulation time step, the complexity of the energy models, the run control settings, and the software and hardware configurations of the computer that is used to make the simulation runs. To demonstrate the approach, simulation runs are performed for several representative DOE-2.1E and EnergyPlus energy models. The computer run time of these energy models are then compared and analyzed.

10acomputer run time10adoe-210aenergyplus10asimulation program1 aHong, Tianzhen1 aBuhl, Walter, F.1 aHaves, Philip1 aSelkowitz, Stephen, E.1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/comparing-computer-run-time-building00466nas a2200133 4500008004100000245007100041210006900112260001200181300001200193490000700205100001700212700002100229856008200250 2008 eng d00aCoupled simulations for naturally ventilated residential buildings0 aCoupled simulations for naturally ventilated residential buildin c05/2008 a386-3980 v171 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/coupled-simulations-naturally00424nas a2200097 4500008004100000245007500041210006900116260003100185100002000216856009000236 2008 eng d00aA Modelica-Based Model Library for Building Energy and Control Systems0 aModelicaBased Model Library for Building Energy and Control Syst aGlasgow, Scotlandc07/20081 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/modelica-based-model-library-building01943nas a2200121 4500008004100000245009400041210006900135260002600204520146700230100002001697700001801717856008601735 2008 eng d00aA Modular Building Controls Virtual Test Bed for the Integration of Heterogeneous Systems0 aModular Building Controls Virtual Test Bed for the Integration o aBerkeley, CAc08/20083 a

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

1 aWetter, Michael1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/modular-building-controls-virtual00454nas a2200133 4500008004100000245004100041210004100082260003100123100002000154700001800174700002500192700002300217856008000240 2008 eng d00aUsing SPARK as a Solver for Modelica0 aUsing SPARK as a Solver for Modelica aBerkeley, CA, USAc07/20081 aWetter, Michael1 aHaves, Philip1 aMoshier, Michael, A.1 aSowell, Edward, F. uhttps://simulationresearch.lbl.gov/publications/using-spark-solver-modelica01363nas a2200145 4500008004100000245004100041210004100082260003100123520089400154100002001048700001801068700002501086700002301111856008301134 2008 eng d00aUsing SPARK as a solver for Modelica0 aUsing SPARK as a solver for Modelica aBerkeley, CA, USAc08/20083 a

Modelica is an object-oriented acausal modeling language that is well positioned to become a de-facto standard for expressing models of complex physical systems. To simulate a model expressed in Modelica, it needs to be translated into executable code. For generating run-time efficient code, such a translation needs to employ algebraic formula manipulations. As the SPARK solver has been shown to be competitive for generating such code but currently cannot be used with the Modelica language, we report in this paper how SPARK's symbolic and numerical algorithms can be implemented in OpenModelica, an open-source implementation of a Modelica modeling and simulation environment. We also report benchmark results that show that for our air flow network simulation benchmark, the SPARK solver is competitive with Dymola, which is believed to provide the best solver for Modelica.

1 aWetter, Michael1 aHaves, Philip1 aMoshier, Michael, A.1 aSowell, Edward, F. uhttp://www.ibpsa.us/simbuild2008/technical_sessions/SB08-DOC-TS03-1-Wetter.pdf00487nas a2200121 4500008004100000245008500041210006900126260002800195100001800223700002000241700001600261856008800277 2007 eng d00aComparison of Co-Simulation Approaches for Building and HVAC/R System Simulation0 aComparison of CoSimulation Approaches for Building and HVACR Sys aBeijing, Chinac09/20071 aTrcka, Marija1 aWetter, Michael1 aHensen, Jan uhttps://simulationresearch.lbl.gov/publications/comparison-co-simulation-approaches01771nas a2200145 4500008004100000245008900041210006900130260002800199300001400227520127100241100001801512700002001530700001601550856005901566 2007 eng d00aComparison of co-simulation approaches for building and HVAC/R system simulation.
0 aComparison of cosimulation approaches for building and HVACR sys aBeijing, Chinac09/2007 a1418-14253 aAppraisal of modern performance-based energy codes, as well as heating, ventilation, air- conditioning and refrigeration (HVAC/R) system*design require use of an integrated building and system performance simulation program. However, the required scope of the modeling library of such integrated tools often goes beyond those offered in available simulation programs. One remedy for this situation would be to develop the required models in an existing simulation program. However, due to the lack of model interoperability, the model would not be available in other simulation programs. We suggest co-simulation for HVAC/R system simulation as an approach to alleviate the above issues. In co-simulation, each subsystem is modeled and simulated in the appropriate simulation program, potentially on different computers, and intermediate results are communicated over the network during execution time. We discuss different co-simulation approaches and give insights into specific prototypes. Based on the prototypes, we compare the approaches in terms of accuracy, stability and execution time, using a simple case study. We finish with results discussions and recommendations on how to perform co-simulation to maintain the required accuracy of simulation results.1 aTrcka, Marija1 aWetter, Michael1 aHensen, Jan uhttp://www.ibpsa.org/proceedings/BS2007/p503_final.pdf00471nas a2200109 4500008004100000245011200041210006900153260001200222100001700234700002100251856008900272 2007 eng d00aA convenient coupled simulation method for thermal environment prediction in naturally ventilated buildings0 aconvenient coupled simulation method for thermal environment pre c09/20071 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/convenient-coupled-simulation-method00417nas a2200109 4500008004100000245006400041210006400105260001200169100001700181700002000198856008900218 2007 eng d00aDiscussion of strategies for UK zero energy building design0 aDiscussion of strategies for UK zero energy building design c09/20071 aWang, Liping1 aGwilliam, Julie uhttps://simulationresearch.lbl.gov/publications/discussion-strategies-uk-zero-energy00740nas 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-air00518nas a2200145 4500008004100000245009100041210006900132260001200201300001200213490000700225100001700232700002100249700001300270856008900283 2007 eng d00aFacade design optimization for naturally ventilated residential buildings in Singapore0 aFacade design optimization for naturally ventilated residential  c08/2007 a954-9610 v391 aWang, Liping1 aWong, Nyuk, Hien1 aLi, Shuo uhttps://simulationresearch.lbl.gov/publications/facade-design-optimization-naturally00576nas a2200133 4500008004100000245013000041210006900171260002800240100002100268700001900289700002400308700002100332856008900353 2007 eng d00aGlobal Efficiency of Direct Flow Vacuum Collectors in Autonomous Solar Desiccant Cooling: Simulation and Experimental Results0 aGlobal Efficiency of Direct Flow Vacuum Collectors in Autonomous aBeijing, Chinac09/20071 aBourdoukan, Paul1 aWurtz, Etienne1 aSpérandio, Maurice1 aJoubert, Patrice uhttps://simulationresearch.lbl.gov/publications/global-efficiency-direct-flow-vacuum00542nas a2200133 4500008004100000245014400041210006900185260001200254300001400266490000700280100001700287700002100304856008300325 2007 eng d00aThe impacts of facade and ventilation strategies on indoor thermal environment for a naturally ventilated residential building in Singapore0 aimpacts of facade and ventilation strategies on indoor thermal e c12/2007 a4006-40150 v421 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/impacts-facade-and-ventilation00519nas a2200133 4500008004100000245009500041210006900136260002500205100001400230700001900244700001500263700001800278856008900296 2007 eng d00aIntegrated Static Pressure Reset with Fan Air Flow Station in Dual-duct VAV System Control0 aIntegrated Static Pressure Reset with Fan Air Flow Station in Du aLong Beach, CAc20071 aWu, Lixia1 aLiu, Mingsheng1 aWang, Gang1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/integrated-static-pressure-reset-fan00524nas a2200133 4500008004100000245012700041210006900168260000900237300001200246490000700258100001700265700002100282856008700303 2007 eng d00aInvestigation of the possibility of applying natural ventilation for thermal comfort in residential buildings in Singapore0 aInvestigation of the possibility of applying natural ventilation c2007 a190-1990 v501 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/investigation-possibility-applying00592nas a2200145 4500008004100000245011600041210006900157260002800226100002300254700001700277700002000294700002300314700002200337856008700359 2007 eng d00aPotential of Buried Pipes Systems and Derived Techniques for Passive Cooling of Buildings in Brazilian Climates0 aPotential of Buried Pipes Systems and Derived Techniques for Pas aBeijing, Chinac09/20071 aHollmuller, Pierre1 aCarlo, Joyce1 aOrdenes, Martin1 aWestphal, Fernando1 aLamberts, Roberto uhttps://simulationresearch.lbl.gov/publications/potential-buried-pipes-systems-and00792nas a2200229 4500008004100000245009200041210006900133300001300202100002900215700001700244700001900261700002000280700001800300700002100318700002200339700001900361700002100380700002300401700003100424700002200455856008500477 2006 eng d00aAdvanced modeling and simulation techniques in MOSILAB: A system development case study0 aAdvanced modeling and simulation techniques in MOSILAB A system  app.63-721 aNytsch-Geusen, Christoph1 aErnst, Thilo1 aSchwarz, Peter1 aVetter, Mathias1 aHolm, Andreas1 aLeopold, Juergen1 aMattes, Alexander1 aNordwig, Andre1 aSchneider, Peter1 aWittwer, Christoph1 aNouidui, Thierry, Stephane1 aSchmidt, Gerhardt uhttps://simulationresearch.lbl.gov/publications/advanced-modeling-and-simulation00535nas a2200133 4500008004100000245011600041210006900157260003200226100001500258700002000273700001300293700001500306856008000321 2006 eng d00aThe Application of Building Energy Simulation and Calibration in Two High-Rise Commercial Buildings in Shanghai0 aApplication of Building Energy Simulation and Calibration in Two aCambridge, MA, USAc08/20061 aPan, Yiqun1 aHuang, Zhizhong1 aWu, Gang1 aChen, Chen uhttps://simulationresearch.lbl.gov/publications/application-building-energy00625nas 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-thermal00618nas a2200145 4500008004100000245013400041210006900175260002900244100001900273700002900292700002400321700001900345700002300364856008500387 2006 eng d00aA Case Study Demonstrating the Utility of Inter-Program Comparative Testing for Diagnosing Errors in Building Simulation Programs0 aCase Study Demonstrating the Utility of InterProgram Comparative aToronto, Canadac05/20061 aWeber, Andreas1 aBeausoleil-Morrison, Ian1 aGriffith, Brent, T.1 aVesanen, Teemu1 aLerson, Sébastien uhttps://simulationresearch.lbl.gov/publications/case-study-demonstrating-utility02020nas a2200265 4500008004100000245010800041210006900149260001200218300001400230490000700244520118600251653003801437653002501475653002401500100002601524700001301550700001601563700001301579700001201592700002401604700002201628700002001650700001301670856007101683 2006 eng d00aCharacterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids0 aCharacterization of the Temperature Oscillation Technique to Mea c08/2006 a2950-29560 v493 a

The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid’s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.

10aTemperature oscillation technique10aThermal conductivity10athermal diffusivity1 aBhattacharya, Prajesh1 aNara, S.1 aVijayan, P.1 aTang, T.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttp://www.sciencedirect.com/science/article/pii/S001793100600144X02020nas a2200265 4500008004100000245010800041210006900149260001200218300001400230490000700244520118600251653003801437653002501475653002401500100002601524700001301550700001601563700001301579700001201592700002401604700002201628700002001650700001301670856007101683 2006 eng d00aCharacterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids0 aCharacterization of the Temperature Oscillation Technique to Mea c08/2006 a2950-29560 v493 a

The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid’s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.

10aTemperature oscillation technique10aThermal conductivity10athermal diffusivity1 aBhattacharya, Prajesh1 aNara, S.1 aVijayan, P.1 aTang, T.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttp://www.sciencedirect.com/science/article/pii/S001793100600144X00467nas a2200109 4500008004100000245010900041210006900150260001200219100001700231700002100248856008800269 2006 eng d00aCoupling between the CFD simulation and building simulation for better prediction of natural ventilation0 aCoupling between the CFD simulation and building simulation for  c04/20061 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/coupling-between-cfd-simulation-and00467nas a2200109 4500008004100000245011100041210006900152260001200221100001700233700002100250856008600271 2006 eng d00aA coupling method to increase the accuracy of natural ventilation prediction in thermal simulation program0 acoupling method to increase the accuracy of natural ventilation  c04/20061 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/coupling-method-increase-accuracy00582nas a2200133 4500008004100000245012900041210006900170260003600239100002000275700002200295700002200317700002200339856008700361 2006 eng d00aDynamic Controls for Energy Efficiency and Demand Response: Framework Concepts and a New Construction Case Study in New York0 aDynamic Controls for Energy Efficiency and Demand Response Frame aPacific Grove, CA, USAc08/20061 aKiliccote, Sila1 aPiette, Mary, Ann1 aWatson, David, S.1 aHughes, Glenn, D. uhttps://simulationresearch.lbl.gov/publications/dynamic-controls-energy-efficiency00546nas a2200121 4500008004100000245012400041210006900165260003200234100002300266700002600289700002300315856008600338 2006 eng d00aExperience Testing EnergyPlus With the IEA HVAC Bestest E300-E545 Series and IEA HVAC Bestest Fuel-Fired Furnace Series0 aExperience Testing EnergyPlus With the IEA HVAC Bestest E300E545 aCambridge, MA, USAc08/20061 aWitte, Michael, J.1 aHenninger, Robert, H.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/experience-testing-energyplus-iea00531nas a2200109 4500008004100000245017300041210006900214260001200283100001700295700002100312856008800333 2006 eng d00aThe impacts of facade designs: orientations, window to wall ratios and shading devices on indoor environment for naturally ventilated residential buildings in Singapore0 aimpacts of facade designs orientations window to wall ratios and c09/20061 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/impacts-facade-designs-orientations01672nas a2200121 4500008004100000245013600041210006900177260003200246520116000278100002001438700002701458856006501485 2006 eng d00aModelica versus TRNSYS — A Comparison Between an Equation-Based and a Procedural Modeling Language for Building Energy Simulation0 aModelica versus TRNSYS A Comparison Between an EquationBased and aCambridge, MA, USAc08/20063 a

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

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

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

10acontaminant transport10amultizone airflow1 aWetter, Michael uhttps://www.modelica.org/events/modelica2006/Proceedings/sessions/Session413.pdf01390nas a2200121 4500008004100000245007300041210006900114260002800183300001200211520094000223100002001163856008501183 2006 eng d00aMultizone Building Model for Thermal Building Simulation in Modelica0 aMultizone Building Model for Thermal Building Simulation in Mode aVienna, Austriac9/2006 a517-5263 aWe present a room model for thermal building simu- lation that we implemented in Modelica. The room model can be used for controls analysis and energy analysis of one or several rooms that are connected through airflow or heat conduction. The room model can assess energy storage in the air and in the build- ing construction materials, heat transfer between the room and the outside environment and the humidity and CO2 release to the room air. The humidity storage in the building construction materials is not modeled. We also describe a novel separation of heat transfer mechanisms on which our room model is built on. The separation allowed a significant reduction in model de- velopment time, and it allows using state-of-the-art programs for computing prior to the thermal building simulation certain energy flows, such as solar heat gain of an active facade without breaking feedback loops between the HVAC system and the room.1 aWetter, Michael uhttps://www.modelica.org/events/modelica2006/Proceedings/sessions/Session5b4.pdf00513nas a2200109 4500008004100000245015400041210006900195260001200264100001700276700002100293856008900314 2006 eng d00aNatural ventilation simulation by using coupling building simulation and CFD simulation program for accurate prediction of indoor thermal environment0 aNatural ventilation simulation by using coupling building simula c09/20061 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/natural-ventilation-simulation-using00435nas a2200109 4500008004100000245008200041210006900123260001200192100001700204700001400221856009000235 2006 eng d00aA numerical study of Trombe wall for enhancing stack ventilation in buildings0 anumerical study of Trombe wall for enhancing stack ventilation i c09/20061 aWang, Liping1 aLi, Angui uhttps://simulationresearch.lbl.gov/publications/numerical-study-trombe-wall-enhancing02402nas a2200145 4500008004100000022001400041245010900055210006900164300001500233490000700248520187300255100001802128700002002146856009002166 2006 eng d a1052-623400aPrecision control for generalized pattern search algorithms with adaptive precision function evaluations0 aPrecision control for generalized pattern search algorithms with a650-669
0 v163 a

In the literature on generalized pattern search algorithms, convergence to a stationary point of a once continuously differentiable cost function is established under the assumption that the cost function can be evaluated exactly. However, there is a large class of engineering problems where the numerical evaluation of the cost function involves the solution of systems of differential algebraic equations. Since the termination criteria of the numerical solvers often depend on the design parameters, computer code for solving these systems usually defines a numerical approximation to the cost function that is discontinuous with respect to the design parameters. Standard generalized pattern search algorithms have been applied heuristically to such problems, but no convergence properties have been stated. In this paper we extend a class of generalized pattern search algorithms to include a subprocedure that adaptively controls the precision of the approximating cost functions. The numerical approximations to the cost function need not define a continuous function. Our algorithms can be used for solving linearly constrained problems with cost functions that are at least locally Lipschitz continuous. Assuming that the cost function is smooth, we prove that our algorithms converge to a stationary point. Under the weaker assumption that the cost function is only locally Lipschitz continuous, we show that our algorithms converge to points at which the Clarke generalized directional derivatives are nonnegative in predefined directions. An important feature of our adaptive precision scheme is the use of coarse approximations in the early iterations, with the approximation precision controlled by a test. We show by numerical experiments that such an approach leads to substantial time savings in minimizing computationally expensive functions.

1 aPolak, Elijah1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/precision-control-generalized-pattern01926nas a2200133 4500008004100000245011300041210006900154300001200223490000700235520142300242100002001665700001801685856008901703 2005 eng d00aBuilding design optimization using a convergent pattern search algorithm with adaptive precision simulations0 aBuilding design optimization using a convergent pattern search a a603-6120 v373 a

We propose a simulation–precision control algorithm that can be used with a family of derivative free optimization algorithms to solve optimization problems in which the cost function is defined through the solutions of a coupled system of differential algebraic equations (DAEs). Our optimization algorithms use coarse precision approximations to the solutions of the DAE system in the early iterations and progressively increase the precision as the optimization approaches a solution. Such schemes often yield a significant reduction in computation time. We assume that the cost function is smooth but that it can only be approximated numerically by approximating cost functions that are discontinuous in the design parameters. We show that this situation is typical for many building energy optimization problems.We present a new building energy and daylighting simulation program, which constructs approximations to the cost function that converge uniformly on bounded sets to a smooth function as precision is increased.We prove that for our simulation program, our optimization algorithms construct sequences of iterates with stationary accumulation points. We present numerical experiments in which we minimize the annual energy consumption of an office building for lighting, cooling and heating. In these examples, our precision control algorithm reduces the computation time up to a factor of four.

1 aWetter, Michael1 aPolak, Elijah uhttps://simulationresearch.lbl.gov/publications/building-design-optimization-using-002352nas a2200121 4500008004100000245008700041210006900128300001400197490000700211520190900218100002002127856008302147 2005 eng d00aBuildOpt - A new building energy simulation program that is built on smooth models0 aBuildOpt A new building energy simulation program that is built  a1085-10920 v403 a

Building energy simulation programs compute numerical approximations to physical phenomena that can be modeled by a system of differential algebraic equations (DAE). For a large class of building energy analysis problems, one can prove that the DAE system has a unique once continuously differentiable solution. Consequently, if building simulation programs are built on models that satisfy the smoothness assumptions required to prove existence of a unique smooth solution, and if their numerical solvers allow controlling the approximation error, one can use such programs with Generalized Pattern Search optimization algorithms that adaptively control the precision of the solutions of the DAE system. Those optimization algorithms construct sequences of iterates with stationary accumulation points and have been shown to yield a significant reduction in computation time compared to algorithms that use fixed precision cost function evaluations. In this paper, we state the required smoothness assumptions and present the theorems that state existence of a unique smooth solution of the DAE system. We present BuildOpt, a detailed thermal and daylighting building energy simulation program. We discuss examples that explain the smoothing techniques used in BuildOpt. We present numerical experiments that compare the computation time for an annual simulation with the smoothing techniques applied to different parts of the models. The experiments show that high precision approximate solutions can only be computed if smooth models are used. This is significant because today's building simulation programs do not use such smoothing techniques and their solvers frequently fail to obtain a numerical solution if the solver tolerances are tight. We also present how BuildOpt's approximate solutions converge to a smooth function as the precision parameter of the numerical solver is tightened.

1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/buildopt-new-building-energy-000634nas a2200181 4500008004100000245008100041210006900122260003100191100001600222700002600238700001300264700001200277700002400289700002200313700002000335700001300355856008400368 2005 eng d00aEffect of Particle Material on the Static Thermal Conductivity of Nanofluids0 aEffect of Particle Material on the Static Thermal Conductivity o aSan Francisco, CAc07/20051 aVijayan, P.1 aBhattacharya, Prajesh1 aNara, S.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttps://simulationresearch.lbl.gov/publications/effect-particle-material-static00624nas a2200157 4500008004100000245013600041210007000177260001200247490000700259100002100266700001700287700002600304700002600330700001700356856009300373 2005 eng d00aEffects of double glazed façade on energy consumption, thermal comfort and condensation for a typical office building in Singapore0 aEffects of double glazed façade on energy consumption thermal co c06/20050 v371 aWong, Nyuk, Hien1 aWang, Liping1 aChandra, Aida, Noplie1 aPandey, Anupama, Rana1 aWei, Xiaolin uhttps://simulationresearch.lbl.gov/publications/effects-double-glazed-fa%C3%A7ade-energy01543nas a2200217 4500008004100000020001800041245013200059210006900191260003100260520078000291100001301071700002601084700001601110700001201126700001801138700002401156700002201180700002001202700001701222856008601239 2005 eng d a0-7918-4221-500aExperimental Determination of the Effect of Varying Base Fluid and Temperature on the Static Thermal Conductivity of Nanofluids0 aExperimental Determination of the Effect of Varying Base Fluid a aOrlando, FLbASMEc11/20053 a

The heat transfer abilities of fluids can be improved by adding small particles of sizes of the order of nanometers. Recently a lot of research has been done in evaluating the thermal conductivity of nanofluids using various nanoparticles. In our present work we address this issue by conducting a series of experiments to determine the effective thermal conductivity of alumina-nanofluids by varying the base fluid with water and antifreeze liquids like ethylene glycol and propylene glycol. Temperature oscillation method is used to find the thermal conductivity of the nanofluid. The results show the thermal conductivity enhancement of nanofluids depends on viscosity of the base fluid. Finally the results are validated with a recently proposed theoretical model.

1 aNara, S.1 aBhattacharya, Prajesh1 aVijayan, P.1 aLai, W.1 aRosenthal, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, Jinlin uhttps://simulationresearch.lbl.gov/publications/experimental-determination-effect00496nas a2200109 4500008004100000245014400041210006900185260000900254100001700263700002100280856008500301 2005 eng d00aThe impacts of facade and ventilation strategies on indoor thermal environment for a naturally ventilated residential building in Singapore0 aimpacts of facade and ventilation strategies on indoor thermal e c20051 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/impacts-facade-and-ventilation-000873nas a2200253 4500008004100000245010600041210006900147260002100216300001500237100002900252700001700281700002100298700002000319700001800339700002100357700001800378700001900396700001900415700002300434700003100457700002200488700002200510856008700532 2005 eng d00aMOSILAB: Development of a modelica based generic simulation tool supporting modal structural dynamics0 aMOSILAB Development of a modelica based generic simulation tool  aHamburg, Germany app.527-5341 aNytsch-Geusen, Christoph1 aErnst, Thilo1 aSchneider, Peter1 aVetter, Mathias1 aHolm, Andreas1 aLeopold, Juergen1 aDoll, Ullrich1 aNordwig, Andre1 aSchwarz, Peter1 aWittwer, Christoph1 aNouidui, Thierry, Stephane1 aSchmidt, Gerhardt1 aMattes, Alexander uhttps://simulationresearch.lbl.gov/publications/mosilab-development-modelica-based00529nas 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-desiccant00472nas a2200109 4500008004100000245011500041210006900156260000900225100001700234700002100251856009000272 2005 eng d00aThermal analysis of climate environments based on weather data in Singapore for naturally ventilated buildings0 aThermal analysis of climate environments based on weather data i c20051 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/thermal-analysis-climate-environments00513nas a2200133 4500008004100000245011300041210006900154260001200223300001200235490000700247100002000254700001800274856008700292 2004 eng d00aBuilding Design Optimization Using a Convergent Pattern Search Algorithm with Adaptive Precision Simulations0 aBuilding Design Optimization Using a Convergent Pattern Search A c09/2004 a603-6120 v371 aWetter, Michael1 aPolak, Elijah uhttps://simulationresearch.lbl.gov/publications/building-design-optimization-using00369nas a2200121 4500008004100000022001500041245003000056210002800086100002000114700001800134700001900152856007600171 2004 eng d aLBNL-5465800aBuildOpt 1.0.1 validation0 aBuildOpt 101 validation1 aWetter, Michael1 aPolak, Elijah1 aCarey, Van, P. uhttps://simulationresearch.lbl.gov/publications/buildopt-101-validation02415nas a2200133 4500008004100000024001500041245008700056210006900143260001200212490000700224520194900231100002002180856008102200 2004 eng d aLBNL-5465700aBuildOpt - A new building energy simulation program that is built on smooth models0 aBuildOpt A new building energy simulation program that is built  c08/20050 v403 a

Building energy simulation programs compute numerical approximations to physical phenomena that can be modeled by a system of differential algebraic equations (DAE). For a large class of building energy analysis problems, one can prove that the DAE system has unique solution that is once continuously differentiable in the building design parameters. Consequently, if building simulation programs are built on models that satisfy the smoothness assumptions required to prove existence of a unique smooth solution, and if their numerical solvers allow controlling the approximation error, one can use such programs with generalized pattern search optimization algorithms that adaptively control the precision of the solutions of the DAE system. Those optimization algorithms construct sequences of iterates with stationary accumulation points and have been shown to yield a significant reduction in computation time compared to algorithms that use fixed precision cost function evaluations. In this paper, we state the required smoothness assumptions and present the theorems that state existence of a unique smooth solution of the DAE system. We present BuildOpt, a detailed thermal and daylighting building energy simulation program. We discuss examples that explain the smoothing techniques used in BuildOpt. We present numerical experiments that compare the computation time for an annual simulation with the smoothing techniques applied to different parts of the models. The experiments show that high precision approximate solutions can only be computed if smooth models are used. This is significant because today's building simulation programs do not use such smoothing techniques and their solvers frequently fail to obtain a numerical solution if the solver tolerances are tight. We also present how BuildOpt's approximate solutions converge to a smooth function as the precision parameter of the numerical solver is tightened.

1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/buildopt-new-building-energy01574nas a2200217 4500008004100000245012300041210006900164260001200233300001200245490000700257520083600264653002201100653001801122653002201140653001901162653001701181653003201198100002001230700002501250856008101275 2004 eng d00aA comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization
0 acomparison of deterministic and probabilistic optimization algor c08/2004 a989-9990 v393 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 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/comparison-deterministic-and01637nas a2200145 4500008004100000245010400041210006900145260001200214300001200226490000700238520111900245100002001364700001801384856008901402 2004 eng d00aA convergent optimization method using pattern search algorithms with adaptive precision simulation0 aconvergent optimization method using pattern search algorithms w c11/2004 a327-3380 v253 a

Thermal building simulation programs, such as EnergyPlus, compute numerical approximations to solutions of systems of differential algebraic equations. We show that the exact solutions of these systems are usually smooth in the building design parameters, but that the numerical approximations are usually discontinuous due to adaptive solvers and finite precision computations. If such approximate solutions are used in conjunction with optimization algorithms that depend on smoothness of the cost function, one needs to compute high precision solutions, which can be prohibitively expensive if used for all iterations. For such situations, we have developed an adaptive simulation–precision control algorithm that can be used in conjunction with a family of derivative free optimization algorithms. We present the main ingredients of the composite algorithms, we prove that the resulting composite algorithms construct sequences with stationary accumulation points, and we show by numerical experiments that using coarse approximations in the early iterations can significantly reduce computation time.

1 aWetter, Michael1 aPolak, Elijah uhttps://simulationresearch.lbl.gov/publications/convergent-optimization-method-using00835nas a2200277 4500008004100000245002600041210002500067260003600092100002300128700002200151700002500173700003000198700002300228700002400251700002400275700002100299700001900320700002600339700001800365700002300383700002100406700002400427700002100451700001500472856007000487 2004 eng d00aEnergyPlus: An Update0 aEnergyPlus An Update aBoulder, Colorado, USAc08/20041 aCrawley, Drury, B.1 aLawrie, Linda, K.1 aPedersen, Curtis, O.1 aWinkelmann, Frederick, C.1 aWitte, Michael, J.1 aStrand, Richard, K.1 aLiesen, Richard, J.1 aBuhl, Walter, F.1 aHuang, Yu, Joe1 aHenninger, Robert, H.1 aGlazer, Jason1 aFisher, Daniel, E.1 aShirley, Don, B.1 aGriffith, Brent, T.1 aEllis, Peter, G.1 aGu, Lixing uhttps://simulationresearch.lbl.gov/publications/energyplus-update00745nas a2200181 4500008004100000245019400041210006900235260002500304100002600329700001600355700001300371700001300384700002400397700002200421700001300443700002000456856008700476 2004 eng d00aEvaluation of the Temperature Oscillation Technique to Calculate Thermal Conductivity of Water and Systematic Measurement of the Thermal Conductivity of Aluminum Oxide – Water Nanofluiids0 aEvaluation of the Temperature Oscillation Technique to Calculate aAnaheim, CAc11/20041 aBhattacharya, Prajesh1 aVijayan, P.1 aTang, T.1 aNara, S.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aWang, J.1 aSong, David, W. uhttps://simulationresearch.lbl.gov/publications/evaluation-temperature-oscillation00520nas a2200121 4500008004100000245009100041210006900132260003600201100002300237700002600260700002300286856008900309 2004 eng d00aExperience Testing EnergyPlus With the ASHRAE 1052-RP Building Fabric Analytical Tests0 aExperience Testing EnergyPlus With the ASHRAE 1052RP Building Fa aBoulder, Colorado, USAc08/20041 aWitte, Michael, J.1 aHenninger, Robert, H.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/experience-testing-energyplus-ashrae01766nas a2200109 4500008004100000022001500041245004800056210004400104520140400148100002001552856008401572 2004 eng d aLBNL-5419900aGenOpt 2.0.0 - Generic optimization program0 aGenOpt 200 Generic optimization program3 a

GenOpt is an optimization program for the minimization of a cost function that is evaluated by an external simulation program. It has been developed for optimization problems where the cost function is computationally expensive and its derivatives are not available or may not even exist. GenOpt can be coupled to any simulation program that reads its input from text files and writes its output to text files. The independent variables can be continuous variables (possibly with lower and upper bounds), discrete variables, or both, continuous and discrete variables. Constraints on dependent variables can be implemented using penalty or barrier functions.

GenOpt has a library with local and global multi-dimensional and one-dimensional optimization algorithms, and algorithms for doing parametric runs. An algorithm interface allows adding new minimization algorithms without knowing the details of the program structure.

GenOpt is written in Java so that it is platform independent. The platform independence and the general interface make GenOpt applicable to a wide range of optimization problems.

GenOpt has not been designed for linear programming problems, quadratic programming problems, and problems where the gradient of the cost function is available. For such problems, as well as for other problems, special tailored software exists that is more efficient.

1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/genopt-200-generic-optimization00502nas a2200133 4500008004100000245009500041210006900136490000600205100001400211700001900225700001700244700001700261856009000278 2004 eng d00aHeat transfer and natural ventilation from single-sided heated solar chimney for buildings0 aHeat transfer and natural ventilation from singlesided heated so0 v31 aLi, Angui1 aJones, Phillip1 aZhao, Pingge1 aWang, Liping uhttps://simulationresearch.lbl.gov/publications/heat-transfer-and-natural-ventilation00439nas a2200109 4500008004100000245009300041210006900134260001200203100001700215700001400232856008300246 2004 eng d00aA numerical study of vertical solar chimney for Enhancing stack ventilation in buildings0 anumerical study of vertical solar chimney for Enhancing stack ve c09/20041 aWang, Liping1 aLi, Angui uhttps://simulationresearch.lbl.gov/publications/numerical-study-vertical-solar00465nas a2200121 4500008004100000245005400041210005400095260003600149100002400185700002400209700002300233856008700256 2004 eng d00aResources for Teaching Building Energy Simulation0 aResources for Teaching Building Energy Simulation aBoulder, Colorado, USAc08/20041 aStrand, Richard, K.1 aLiesen, Richard, J.1 aWitte, Michael, J. uhttps://simulationresearch.lbl.gov/publications/resources-teaching-building-energy00423nas a2200109 4500008004100000245005000041210004900091260005800140490001000198100002000208856008500228 2004 eng d00aSimulation-Based Building Energy Optimization0 aSimulationBased Building Energy Optimization aBerkeley, CA, USAbUniversity of California, Berkeley0 vPh.D.1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/simulation-based-building-energy02585nas a2200109 4500008004100000245005000041210004900091520221100140653001702351100002002368856008702388 2004 eng d00aSimulation-based building energy optimization0 aSimulationbased building energy optimization3 a

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

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

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

10adissertation1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/simulation-based-building-energy-002172nas 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-000504nas a2200133 4500008004100000245008500041210006900126300001200195490000700207100002500214700001800239700002500257856008800282 2003 eng d00aField Testing Model-Based Condition Monitoring on a HVAC Cooling Coil Sub-System0 aField Testing ModelBased Condition Monitoring on a HVAC Cooling  a103-1160 v241 aBuswell, Richard, A.1 aHaves, Philip1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/field-testing-model-based-condition00551nas a2200133 4500008004100000245010500041210006900146260002900215100002500244700001800269700002200287700002500309856008300334 2002 eng d00aNon-Linear Recursive Parameter Estimation Applied to Fault Detection and Diagnosis in Real Buildings0 aNonLinear Recursive Parameter Estimation Applied to Fault Detect aLiège, Belgiumc12/20021 aBuswell, Richard, A.1 aHaves, Philip1 aSalsbury, Tim, I.1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/non-linear-recursive-parameter02364nas a2200169 4500008004100000245004400041210004200085260001900127300001200146490000600158520188400164100002002048700002202068700002602090700001602116856006202132 2001 eng d00aGenOpt - A Generic Optimization Program0 aGenOpt A Generic Optimization Program aRio de Janeiro a601-6080 vI3 a

The potential offered by computer simulation is often not realized: Due to the interaction of system variables, simulation users rarely know how to choose input parameter settings that lead to optimal performance of a given system. Thus, a program called GenOpt® that automatically determines optimal parameter settings has been developed.

GenOpt is a generic optimization program. It minimizes an objective function with respect to multiple parameters. The objective function is evaluated by a simulation program that is iteratively called by GenOpt. In thermal building simulation — which is the main target of GenOpt — the simulation program usually has text-based I/O. The paper shows how GenOpt's simulation program interface allows the coupling of any simulation program with text based I/O by simply editing a configuration file, avoiding code modification of the simulation program. By using object-oriented programming, a high-level interface for adding minimization algorithms to GenOpt's library has been developed. We show how the algorithm interface separates the minimization algorithms and GenOpt's kernel, which allows implementing additional algorithms without being familiar with the kernel or having to recompile it. The algorithms can access utility classes that are commonly used for minimization, such as optimality check, line-search, etc.

GenOpt has successfully solved various optimization problems in thermal building simulation. We show an example of minimizing source energy consumption of an office building using EnergyPlus, and of minimizing auxiliary electric energy of a solar domestic hot water system using TRNSYS. For both examples, the time required to set up the optimization was less than one hour, and the energy savings are about 15%, together with better daylighting usage or lower investment costs, respectively.

1 aWetter, Michael1 aLamberts, Roberto1 aNegrão, Cezar, O. R.1 aHensen, Jan uhttp://www.ibpsa.org/proceedings/BS2001/BS01_0601_608.pdf00468nas a2200121 4500008004100000245007400041210006900115490000800184100001800192700002200210700002500232856008900257 1996 eng d00aCondition Monitoring in HVAC Subsystems using First Principles Models0 aCondition Monitoring in HVAC Subsystems using First Principles M0 v1021 aHaves, Philip1 aSalsbury, Tim, I.1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/condition-monitoring-hvac-subsystems00506nas a2200121 4500008004100000245009300041210006900134260002700203100002200230700001800252700002500270856008900295 1995 eng d00aA Fault Detection and Diagnosis Method Based on First Principles Models and Expert Rules0 aFault Detection and Diagnosis Method Based on First Principles M aBejing, Chinac09/19951 aSalsbury, Tim, I.1 aHaves, Philip1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/fault-detection-and-diagnosis-method00474nas a2200121 4500008004100000245008700041210006900128490000800197100001900205700001800224700002000242856009000262 1994 eng d00aDesign, Construction and Commissioning of Building Emulators for EMCS Applications0 aDesign Construction and Commissioning of Building Emulators for 0 v1001 aWang, Shengwei1 aHaves, Philip1 aNusgens, Pierre uhttps://simulationresearch.lbl.gov/publications/design-construction-and-commissioning00561nas a2200145 4500008004100000245010900041210006900150490000800219100002300227700001900250700001800269700002100287700002000308856008700328 1994 eng d00aInvestigation of the Reliability of Building Emulators for Testing Energy Management and Control Systems0 aInvestigation of the Reliability of Building Emulators for Testi0 v1001 aPeitsman, Henk, C.1 aWang, Shengwei1 aHaves, Philip1 aKärki, Satu, H.1 aPark, Cheol, P. uhttps://simulationresearch.lbl.gov/publications/investigation-reliability-building00600nas a2200157 4500008004100000245008700041210006900128260002900197100002300226700002300249700002400272700001800296700002200314700002500336856008100361 1994 eng d00aModel-Based Approaches to Fault Detection and Diagnosis in Air-Conditioning System0 aModelBased Approaches to Fault Detection and Diagnosis in AirCon aLiège, Belgiumc12/19941 aBenouarets, Mourad1 aDexter, Arthur, L.1 aFargus, Richard, S.1 aHaves, Philip1 aSalsbury, Tim, I.1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/model-based-approaches-fault01682nas a2200193 4500008004100000245009600041210006900137260002600206300001200232520101800244100002501262700001501287700001801302700002301320700002201343700002001365700001901385856008401404 1991 eng d00aUse of Building Emulators to Evaluate the Performance of Building Energy Management Systems0 aUse of Building Emulators to Evaluate the Performance of Buildin aNice, Francec08/1991 a209-2133 a

Three complementary approaches may be used in the evaluation of the performance of building control systems-simulation, emulation and field testing. In emulation a real-time simulation of the building and HVAC plant is connected to a real building energy management system (BEMS) via a hardware interface. Emulation has the advantage of allowing controlled, repeatable experiments whilst testing real devices that may contain proprietary algorithms. Building emulators have been developed by the authors in the context of lEA Annex 17, which is concerned with the use of simulation to evaluate the performance of BEMS. The paper discusses different approaches to the design of building emulators and describes the different architectures, hardware and software used by the authors. The problem of evaluating the overall performance of BEMS is discussed and results are presented that illustrate the use of emulators to investigate the influence of the tuning of local loop controls on building performance.

1 aVaezi-Nejad, Hossein1 aHutter, E.1 aHaves, Philip1 aDexter, Arthur, L.1 aKelly, George, E.1 aNusgens, Pierre1 aWang, Shengwei uhttps://simulationresearch.lbl.gov/publications/use-building-emulators-evaluate00555nas 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.pdf00671nas a2200193 4500008003900000245006700039210006200106260004300168490000800211100002400219700002100243700002400264700001700288700001600305700002200321700002100343700003000364856008300394 1985 d00aThe DOE-2 Computer Program for Thermal Simulation of Buildings0 aDOE2 Computer Program for Thermal Simulation of Buildings bAmerican Institute of Physicsc01/19850 v1351 aBirdsall, Bruce, E.1 aBuhl, Walter, F.1 aCurtis, Richard, B.1 aErdem, Ender1 aEto, Joseph1 aHirsch, James, J.1 aOlson, Karen, H.1 aWinkelmann, Frederick, C. uhttps://simulationresearch.lbl.gov/publications/doe-2-computer-program-thermal00539nas a2200145 4500008003900000245005700039210005500096260007100151100002100222700001700243700002000260700002200280700003000302856006100332 1985 d00aNew Features of the DOE-2.1c Energy Analysis Program0 aNew Features of the DOE21c Energy Analysis Program bInternational Building Performance Simulation Associationc01/19851 aBuhl, Walter, F.1 aErdem, Ender1 aEto, Joseph, H.1 aHirsch, James, J.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1985/BS85_195_200.pdf00595nas a2200181 4500008003900000245004700039210004200086260002300128100002400151700002400175700002100199700001700220700002000237700002200257700002100279700003000300856008300330 1984 d00aThe DOE-2 Building Energy Analysis Program0 aDOE2 Building Energy Analysis Program aSingaporec05/19841 aCurtis, Richard, B.1 aBirdsall, Bruce, E.1 aBuhl, Walter, F.1 aErdem, Ender1 aEto, Joseph, H.1 aHirsch, James, J.1 aOlson, Karen, H.1 aWinkelmann, Frederick, C. uhttps://simulationresearch.lbl.gov/publications/doe-2-building-energy-analysis01668nas a2200193 4500008004100000245003800041210003400079260001200113300001200125490000800137520111500145100002201260700001801282700002601300700002001326700002501346700002401371856007901395 1974 eng d00aThe Radio Polarization of Quasars0 aRadio Polarization of Quasars c07/1974 a137-1620 v1683 a

Observations over a wide range of wavelengths, 2.2 ≤ λ ≤ 73 cm, have been combined to define the wavelength variation of the degree of linear polarization m(λ) for 120 quasars with known redshift. For the majority, m(λ) decreases monotonically with increasing wavelength but for 35 sources the polarization curve is inverted at short wavelengths. A classification is given, based on both the polarization curve and the radio spectrum, and the results are interpreted in terms of the presence or absence of opaque components in the source. The depolarization which occurs at long wavelengths is accounted for by a combination of spectral effects and Faraday depolarization. For 46 steep-spectrum sources the depolarization curve appears to be dominated by the Faraday effect, and has been used to deduce the electron density within the radiating components. In this group of sources the correlation between depolarization and redshift noted by Kronberg et al. is confirmed and strengthened. A discussion is given of some theoretical models of radio sources in the light of the depolarization data.

1 aConway, Robin, G.1 aHaves, Philip1 aKronberg, Philipp, P.1 aStannard, David1 aVallée, Jacques, P.1 aWardle, John, F. C. uhttps://simulationresearch.lbl.gov/publications/radio-polarization-quasars