02259nas 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/S030626191930749402000nas a2200253 4500008004100000022001300041245010400054210006900158260001200227300001100239490000700250520118500257653002701442653001801469653001601487653002501503653002001528100003001548700001501578700001901593700001901612700002901631856008601660 2018 eng d a1550485900aA Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems0 aFramework for PrivacyPreserving Data Publishing with Enhanced Ut c12/2018 a1 - 220 v143 a

Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.

10acyber physical systems10adeep learning10ak-anonymity10aPrivacy preservation10aSmart buildings1 aSangogboye, Fisayo, Caleb1 aJia, Ruoxi1 aHong, Tianzhen1 aSpanos, Costas1 aKjærgaard, Mikkel, Baun uhttps://simulationresearch.lbl.gov/publications/framework-privacy-preserving-data02757nas a2200109 4500008004100000245011400041210006900155520229900224100001502523700001902538856009002557 2017 eng d00aA Framework for Quantifying the Impact of Occupant Behavior on Energy Savings of Energy Conservation Measures0 aFramework for Quantifying the Impact of Occupant Behavior on Ene3 a

To improve energy efficiency—during new buildings design or during a building retrofit—evaluating the energy savings potential of energy conservation measures (ECMs) is a critical task. In building retrofits, occupant behavior significantly impacts building energy use and is a leading factor in uncertainty when determining the effectiveness of retrofit ECMs. Current simulation-based assessment methods simplify the representation of occupant behavior by using a standard or representative set of static and homogeneous assumptions ignoring the dynamics, stochastics, and diversity of occupant's energy-related behavior in buildings. The simplification contributes to significant gaps between the simulated and measured actual energy performance of buildings.

This study presents a framework for quantifying the impact of occupant behaviors on ECM energy savings using building performance simulation. During the first step of the study, three occupant behavior styles (austerity, normal, and wasteful) were defined to represent different levels of energy consciousness of occupants regarding their interactions with building energy systems (HVAC, windows, lights and plug-in equipment). Next, a simulation workflow was introduced to determine a range of the ECM energy savings. Then, guidance was provided to interpret the range of ECM savings to support ECM decision making. Finally, a pilot study was performed in a real building to demonstrate the application of the framework. Simulation results show that the impact of occupant behaviors on ECM savings vary with the type of ECM. Occupant behavior minimally affects energy savings for ECMs that are technology-driven (the relative savings differ by less than 2%) and have little interaction with the occupants; for ECMs with strong occupant interaction, such as the use of zonal control variable refrigerant flow system and natural ventilation, energy savings are significantly affected by occupant behavior (the relative savings differ by up to 20%). The study framework provides a novel, holistic approach to assessing the uncertainty of ECM energy savings related to occupant behavior, enabling stakeholders to understand and assess the risk of adopting energy efficiency technologies for new and existing buildings.

1 aSun, Kaiyu1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/framework-quantifying-impact-occupant03302nas a2200229 4500008003900000245012400039210006900163260005100232300001200283490000800295520248200303653003102785653002402816653001502840653002802855653003202883653001702915100001902932700002002951700001802971856008302989 2013 d00aA Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather Data0 aFresh Look at Weather Impact on Peak Electricity Demand and Ener bLawrence Berkeley National Laboratoryc11/2013 a333-3500 v1113 a

Buildings consume more than one third of the world’s total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980–2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: (1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; (2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; (3) the weather impact is greater for buildings in colder climates than warmer climates; (4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and (5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

10aActual meteorological year10abuilding simulation10aenergy use10aPeak electricity demand10aTypical meteorological year10aweather data1 aHong, Tianzhen1 aChang, Wen-Kuei1 aLin, Hung-Wen uhttps://simulationresearch.lbl.gov/publications/fresh-look-weather-impact-peak01616nas 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-import01987nas 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-time00492nas a2200121 4500008004100000245011800041210006900159300001200228490000700240100001600247700001800263856008900281 2010 eng d00aFast and informative flow simulation in a building by using fast fluid dynamics model on graphics processing unit0 aFast and informative flow simulation in a building by using fast a747-7570 v451 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/fast-and-informative-flow-simulation00394nas a2200109 4500008004100000245005200041210005200093260002100145100001600166700001800182856008400200 2010 eng d00aFast simulation of smoke transport in buildings0 aFast simulation of smoke transport in buildings aBeograd, Serbian1 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/fast-simulation-smoke-transport00423nas a2200109 4500008004100000245006700041210006700108260001700175100001600192700001800208856008700226 2009 eng d00aFast parallelized flow simulations on graphic processing units0 aFast parallelized flow simulations on graphic processing units aBusan, Korea1 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/fast-parallelized-flow-simulations01212nas a2200145 4500008004100000245009100041210006900132260001200201520069300213100002000906700002500926700002400951700001800975856007300993 2008 eng d00aFault Diagnostics and Supervised Testing: How Fault Diagnostic tools can be Proactive?0 aFault Diagnostics and Supervised Testing How Fault Diagnostic to c11/20083 aThe topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.1 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttp://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=50300518nas 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-naturally00337nas a2200097 4500008004100000245003300041210003300074260003600107100002100143856007500164 2004 eng d00aFlow in an Underfloor Plenum0 aFlow in an Underfloor Plenum aBoulder, Colorado, USAc08/20041 aLinden, Paul, F. uhttps://simulationresearch.lbl.gov/publications/flow-underfloor-plenum00484nas a2200121 4500008004100000245010400041210006900145260001200214490000700226100002400233700001800257856008700275 2004 eng d00aFramework for Coupling Room Air Models to Heat Balance Model Load and Energy Calculations (RP-1222)0 aFramework for Coupling Room Air Models to Heat Balance Model Loa c04/20040 v101 aGriffith, Brent, T.1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/framework-coupling-room-air-models00504nas 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-condition01390nas a2200133 4500008004100000024001700041245011000058210006900168260003900237520086100276100001301137700001801150856008801168 2002 eng d aLBNL - 5067800aField Testing of Component-Level Model-Based Fault Detection Methods for Mixing Boxes and VAV Fan Systems0 aField Testing of ComponentLevel ModelBased Fault Detection Metho aAsilomar, California, USAc08/20023 a

An automated fault detection and diagnosis tool for HVAC systems is being developed, based on an integrated, lifecycle, approach to commissioning and performance monitoring. The tool uses component-level HVAC equipment models implemented in the SPARK equation-based simulation environment. The models are configured using design information and component manufacturers' data and then fine-tuned to match the actual performance of the equipment by using data measured during functional tests of the sort using in commissioning. This paper presents the results of field tests of mixing box and VAV fan system models in an experimental facility and a commercial office building. The models were found to be capable of representing the performance of correctly operating mixing box and VAV fan systems and detecting several types of incorrect operation.

1 aXu, Peng1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/field-testing-component-level-model01395nas a2200133 4500008004100000050001500041245011000056210006900166260003900235520086600274100001301140700001801153856009001171 2002 eng d aLBNL-5067800aField Testing of Component-Level Model-Based Fault Detection Methods for Mixing Boxes and VAV Fan Systems0 aField Testing of ComponentLevel ModelBased Fault Detection Metho aPacific Grove, Californiac05/20023 a

An automated fault detection and diagnosis tool for HVAC systems is being developed, based on an integrated, life-cycle, approach to commissioning and performance monitoring. The tool uses component-level HVAC equipment models implemented in the SPARK equation-based simulation environment. The models are configured using design information and component manufacturers' data and then fine-tuned to match the actual performance of the equipment by using data measured during functional tests of the sort using in commissioning. This paper presents the results of field tests of mixing box and VAV fan system models in an experimental facility and a commercial office building. The models were found to be capable of representing the performance of correctly operating mixing box and VAV fan systems and detecting several types of incorrect operation.

1 aXu, Peng1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/field-testing-component-level-model-000904nas a2200109 4500008004100000245005500041210005400096260003600150520053200186100001800718856005800736 1997 eng d00aFault Modelling in Component-based HVAC Simulation0 aFault Modelling in Componentbased HVAC Simulation aPrague, Czech Republicc09/19973 aModels of faulty components or processes may either be used on-line as part of a fault detec- tion and diagnosis (FDD) system or may be used in simulations to train or test FDD procedures. Some faults may be modelled by choosing suit- able values of the parameters of fault free models, whereas other faults require specific extensions to fault free models. An example of the modelling of various faults in a cooling coil subsystem is pre- sented and different methods of using simulation in testing and training are discussed.1 aHaves, Philip uhttp://www.ibpsa.org/proceedings/BS1997/BS97_P101.pdf00506nas 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-method00468nas a2200121 4500008004100000245007000041210006700111260001800178100002300196700002400219700001800243856008500261 1994 eng d00aFault Detection in Air-Conditioning Systems Using A.I. Techniques0 aFault Detection in AirConditioning Systems Using AI Techniques aYork, England1 aDexter, Arthur, L.1 aFargus, Richard, S.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/fault-detection-air-conditioning