01972nas a2200145 4500008004100000245010000041210006900141260001200210520143000222100002401652700002601676700001501702700001901717856009001736 2019 eng d00aOPEN COMPUTING INFRASTRUCTURE FOR SHARING DATA ANALYTICS TO SUPPORT BUILDING ENERGY SIMULATIONS0 aOPEN COMPUTING INFRASTRUCTURE FOR SHARING DATA ANALYTICS TO SUPP c08/20193 a

Building energy simulation plays an increasingly important role in building design and operation. In this paper, we present an open computing infrastructure, Virtual Information Fabric Infrastructure (VIFI), that allows building designers and engineers to enhance their simulations by combining empirical data with diagnostic or prognostic models. Based on the idea of dynamic data-driven application systems (DDDAS), the VIFI infrastructure complements conventional data-centric sharing strategies and addresses key data sharing concerns such as the privacy of building occupants. To demonstrate the potential of the VIFI infrastructure, we simulate an empirically-derived lighting schedule in the U.S. Department of Energy's small office building reference model. We use the case study simulation to explore the possibility and potential of integrating data-centric and analytic-centric sharing strategies; the method of combining empirical data with simulations; the creation, sharing, and execution of analytics using VIFI; and the impact of incorporating empirical data on energy simulations. While the case study reveals clear advantages of the VIFI data infrastructure, research questions remain surrounding the motivation and benefits for sharing data, the metadata that are required to support the composition of analytics, and the performance metrics that could be used in assessing the applications of VIFI.

1 aKaraguzel, Omer, T.1 aElshambakey, Mohammed1 aZhu, Yimin1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/open-computing-infrastructure-sharing02356nas 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.pdf02042nas a2200193 4500008004100000245014000041210006900181520132600250653002201576653003601598653001801634653001501652653002201667100001901689700001701708700001901725700001801744856008601762 2017 eng d00aOccupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs0 aOccupant behavior models A critical review of implementation and3 a

Occupant behavior (OB) in buildings is a leading factor influencing energy use in buildings. Quantifying this influence requires the integration of OB models with building performance simulation (BPS). This study reviews approaches to representing and implementing OB models in today’s popular BPS programs, and discusses weaknesses and strengths of these approaches and key issues in integrating of OB models with BPS programs. Two key findings are: (1) a common data model is needed to standardize the representation of OB models, enabling their flexibility and exchange among BPS programs and user applications; the data model can be implemented using a standard syntax (e.g., in the form of XML schema), and (2) a modular software implementation of OB models, such as functional mock-up units for co-simulation, adopting the common data model, has advantages in providing a robust and interoperable integration with multiple BPS programs. Such common OB model representation and implementation approaches help standardize the input structures of OB models, enable collaborative development of a shared library of OB models, and allow for rapid and widespread integration of OB models with BPS programs to improve the simulation of occupant behavior and quantification of their impact on building performance.

10aBehavior Modeling10abuilding performance simulation10aco-simulation10adata model10aoccupant behavior1 aHong, Tianzhen1 aChen, Yixing1 aBelafi, Zsofia1 aD'Oca, Simona uhttps://simulationresearch.lbl.gov/publications/occupant-behavior-models-critical01835nas a2200169 4500008003900000245005800039210005700097520128500154653001601439653002101455653002501476653002201501100001501523700001901538700002701557856008101584 2016 d00aOccupancy data analytics and prediction: a case study0 aOccupancy data analytics and prediction a case study3 a

Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.

10adata mining10aMachine learning10aoccupancy prediction10aoccupant presence1 aLiang, Xin1 aHong, Tianzhen1 aShen, Geoffrey, Qiping uhttps://simulationresearch.lbl.gov/publications/occupancy-data-analytics-and02039nas a2200217 4500008003900000245007300039210006900112260001200181300001200193490000700205520135500212653002301567653002401590653001601614653002301630653002201653653002001675100001801695700001901713856008901732 2015 d00aOccupancy Schedules Learning Process Through a Data Mining Framework0 aOccupancy Schedules Learning Process Through a Data Mining Frame c02/2015 a395-4080 v883 a

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 min interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. The identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.

10aBehavioral Pattern10abuilding simulation10adata mining10aOccupancy schedule10aoccupant behavior10aOffice Building1 aD'Oca, Simona1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/occupancy-schedules-learning-process01728nas a2200265 4500008003900000245010500039210006900144260001200213300001200225490000800237520089300245653002401138653002201162653002001184653001501204653002201219100001201241700002101253700001901274700001901293700001701312700002301329700002201352856008801374 2015 d00aOccupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges0 aOccupant Behavior Modeling for Building Performance Simulation C c11/2015 a264-2780 v1073 a

Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs.

10abuilding simulation10aenergy efficiency10aenergy modeling10aenergy use10aoccupant behavior1 aYan, Da1 aO'Brien, William1 aHong, Tianzhen1 aFeng, Xiaohang1 aGunay, Burak1 aTahmasebi, Farhang1 aMahdavi, Ardeshir uhttps://simulationresearch.lbl.gov/publications/occupant-behavior-modeling-building02313nas a2200265 4500008003900000245014200039210006900181260001200250300001200262490000700274520142200281653003201703653002401735653002001759653001001779653002201789653001501811100001901826700001801845700002801863700002701891700001701918700002601935856008601961 2015 d00aAn Ontology to Represent Energy-Related Occupant Behavior in Buildings. Part II: Implementation of the DNAS framework using an XML schema0 aOntology to Represent EnergyRelated Occupant Behavior in Buildin c08/2015 a196-2050 v943 a

Energy-related occupant behavior in buildings is difficult to define and quantify, yet critical to our understanding of total building energy consumption. Part I of this two-part paper introduced the DNAS (Drivers, Needs, Actions and Systems) framework, to standardize the description of energy-related occupant behavior in buildings. Part II of this paper implements the DNAS framework into an XML (eXtensible Markup Language) schema, titled ‘occupant behavior XML’ (obXML). The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools. The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior, linking three main elements representing Buildings, Occupants and Behaviors. Using the schema structure, the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML. The obXML schema has inherent flexibility to represent numerous, diverse and complex types of occupant behaviors in buildings, and it can also be expanded to encompass new types of behaviors. The implementation of the DNAS framework into the obXML schema will facilitate the development of occupant information modeling (OIM) by providing interoperability between occupant behavior models and building energy modeling programs.

10abuilding energy consumption10abuilding simulation10aenergy modeling10aobXML10aoccupant behavior10aXML schema1 aHong, Tianzhen1 aD'Oca, Simona1 aTaylor-Lange, Sarah, C.1 aTurner, William, J. N.1 aChen, Yixing1 aCorgnati, Stefano, P. uhttps://simulationresearch.lbl.gov/publications/ontology-represent-energy-related02575nas a2200241 4500008003900000245011800039210006900157260001200226300001200238490000700250520177500257653002002032653003802052653001302090653002202103653001302125653001502138100001902153700001802172700002702190700002802217856008802245 2015 d00aAn Ontology to Represent Energy-related Occupant Behavior in Buildings Part I: Introduction to the DNAs Framework0 aOntology to Represent Energyrelated Occupant Behavior in Buildin c10/2015 a764-7770 v923 a

Reducing energy consumption in the buildings sector requires significant changes, but technology alone may fail to guarantee efficient energy performance. Human behavior plays a pivotal role in building design, operation, management and retrofit, and is a crucial positive factor for improving the indoor environment, while reducing energy use at low cost. Over the past 40 years, a substantial body of literature has explored the impacts of human behavior on building technologies and operation. Often, need-action-event cognitive theoretical frameworks were used to represent human-machine interactions. In Part I of this paper a review of more than 130 published behavioral studies and frameworks was conducted. A large variety of data-driven behavioral models have been developed based on field monitoring of the human-building-system interaction. Studies have emerged scattered geographically around the world that lack in standardization and consistency, thus leading to difficulties when comparing one with another. To address this problem, an ontology to represent energy-related occupant behavior in buildings is presented. Accordingly, the technical DNAs framework is developed based on four key components: i) the Drivers of behavior, ii) the Needs of the occupants, iii) the Actions carried out by the occupants, and iv) the building systems acted upon by the occupants. This DNAs framework is envisioned to support the international research community to standardize a systematic representation of energy-related occupant behavior in buildings. Part II of this paper further develops the DNAs framework as an XML (eXtensible Markup Language) schema, obXML, for exchange of occupant information modeling and integration with building simulation tools.

10aBuilding energy10ahuman-building-system interaction10amodeling10aoccupant behavior10aontology10asimulation1 aHong, Tianzhen1 aD'Oca, Simona1 aTurner, William, J. N.1 aTaylor-Lange, Sarah, C. uhttps://simulationresearch.lbl.gov/publications/ontology-represent-energy-related-002149nas a2200181 4500008004100000022002200041245021800063210006900281260002400350300001500374490000600389520138300395100003101778700002901809700001801838700002101856856009001877 2008 eng d a978-87-7877-265-700aObject-oriented hygrothermal building physics library as a tool to predict and to ensure a thermal and hygric indoor comfort in building construction by using a Predicted-Mean-Vote (PMV) control ventilation system0 aObjectoriented hygrothermal building physics library as a tool t aCopenhagen, Denmark app.825-8320 v23 a

The indoor temperature and humidity conditions of the building envelope are important parameters for the evaluation of the thermal and hygric indoor comfort. In the research project GENSIM a new hygrothermal building library, based on the object- and equation-oriented model description language Modelica® has been developed by the Fraunhofer Institutes IBP and FIRST. This library includes many models as for instance a hygrothermal wall model, an air volume model, a zone model, a window model and an environment model. Due to the object-oriented modelling approach, some models of this library can be configured to a complex hygrothermal room model, which can predict the time dependent indoor temperature and humidity conditions in a building construction. In this paper we will introduce in a first step the object-oriented hygrothermal room model of this library. In a second step, the validation of the room model with some field experiments will be shown. In a third step we willpresent some simulation results, we obtained by coupling the room model with an implemented Predicted-Mean-Vote (PMV) control ventilation system to predict and to ensure a thermal and hygric indoor comfort in one case study. In the conclusion, the possible range of future applications of this new hygrothermal building physics library and demands for further research are indicated.

1 aNouidui, Thierry, Stephane1 aNytsch-Geusen, Christoph1 aHolm, Andreas1 aSedlbauer, Klaus uhttps://simulationresearch.lbl.gov/publications/object-oriented-hygrothermal-building01442nas a2200145 4500008004100000245008800041210006900129260001000198520092700208100002001135700002501155700002401180700001801204856007401222 2008 eng d00aOvercoming the Complexity of Diagnostic Problems due to Sensor Network Architecture0 aOvercoming the Complexity of Diagnostic Problems due to Sensor N c11/083 a

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

1 aNajafi, Massieh1 aAuslander, David, M.1 aBartlett, Peter, L.1 aHaves, Philip uhttps://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=50301552nas a2200205 4500008004100000245007600041210006900117260000900186300001200195490000700207520087100214653002901085653003001114653002601144653002401170653003701194100001901231700001401250856008201264 1998 eng d00aOutdoor synthetic temperature for the calculation of space heating load0 aOutdoor synthetic temperature for the calculation of space heati c1998 a269-2770 v283 a

Methods to select the outdoor design temperature (ODT) for heating load calculation specified in current design codes in different countries are firstly discussed. Then a new method namely Stochastic Analysis is presented to determine the outdoor synthetic temperature (OST), which fully considers the randomness of weather and internal casual gains, and the thermal performance of buildings. The concept of OST enables the design of space heating system to be the trade-off between economics and risk. Finally, case studies of the influence of different building components on OST of a residential room in Beijing have been studied, which shows that OST depends upon building structures as well as weather conditions. It is recommended that OST rather than ODT should be employed in heating load calculation hence, sizing equipment for space heating systems.

10aheating load calculation10aoutdoor design conditions10aresidential buildings10astochastic analysis10athermal performance of buildings1 aHong, Tianzhen1 aJiang, Yi uhttps://simulationresearch.lbl.gov/publications/outdoor-synthetic-temperature00395nas a2200097 4500008003900000245006400039210006300103260002500166100002000191856008600211 1983 d00aOptimal Cogeneration Systems for High-Rise Office Buildings0 aOptimal Cogeneration Systems for HighRise Office Buildings aOrlando, FLc05/19831 aEto, Joseph, H. uhttps://simulationresearch.lbl.gov/publications/optimal-cogeneration-systems-high00416nas a2200097 4500008004100000245007200041210006900113260002900182100001800211856008900229 1979 eng d00aOne Dimensional Representations of the Heat Flow Beneath a Building0 aOne Dimensional Representations of the Heat Flow Beneath a Build aSan Antonio, TXc03/19791 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/one-dimensional-representations-heat01326nas a2200229 4500008004100000245005900041210005500100260001200155300001200167490000800179520057600187653003200763653003400795653002500829653003300854653004000887653002500927653001900952100001800971700002200989856008501011 1975 eng d00aThe Orientation of the Magnetic Field in Radio Sources0 aOrientation of the Magnetic Field in Radio Sources c11/1975 a53P-56P0 v1733 a

Recent data on the polarization of extragalactic radio sources are used to investigate the distribution of Delta, the angle between the major axis of a source and the intrinsic position angle of the E vector of linear polarization. Previous work on this subject has led to widely divergent conclusions. It is found that sources of high radio luminosity usually have Delta near 90 deg, implying that the magnetic fields in such sources are oriented along the major axis. For radio galaxies with low luminosity, on the other hand, Delta tends to lie nearer zero deg.

10aextragalactic radio sources10amagnetic field configurations10apolarization (waves)10apolarization characteristics10apolarized electromagnetic radiation10aradiant flux density10aradio galaxies1 aHaves, Philip1 aConway, Robin, G. uhttps://simulationresearch.lbl.gov/publications/orientation-magnetic-field-radio