@article {31673, title = {OPEN COMPUTING INFRASTRUCTURE FOR SHARING DATA ANALYTICS TO SUPPORT BUILDING ENERGY SIMULATIONS}, year = {2019}, month = {08/2019}, abstract = {

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{\textquoteright}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.

}, author = {Omer T. Karaguzel and Mohammed Elshambakey and Yimin Zhu and Tianzhen Hong} } @article {31309, title = {Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings}, journal = {Automation in Construction}, volume = {94}, year = {2018}, month = {10/2018}, pages = {233 - 243}, abstract = {

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{\textquoteright} 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{\textquoteright} 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.

}, keywords = {data fusion, environmental sensing, Machine learning, occupancy prediction, Wi-Fi sensing}, issn = {09265805}, doi = {10.1016/j.autcon.2018.07.007}, url = {https://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/plain}, author = {Wei Wang and Jiayu Chen and Tianzhen Hong} } @article {31304, title = {Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology}, journal = {Building and Environment}, volume = {138}, year = {2018}, month = {06/2018}, pages = {160 - 170}, abstract = {

Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings{\textquoteright} 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{\textquoteright}s occupancy profile.

}, issn = {03601323}, doi = {10.1016/j.buildenv.2018.04.034}, url = {https://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/plain}, author = {Wei Wang and Jiayu Chen and Tianzhen Hong and Na Zhu} } @conference {31688, title = {OpenBuildingControl: Modeling Feedback Control as a Step Towards Formal Design, Specification, Deployment and Verification of Building Control Sequences}, booktitle = {2018 Building Performance Modeling Conference and SimBuild co-organized by ASHRAE and IBPSA-USA}, year = {2018}, month = {09/2018}, address = {Chicago, IL}, abstract = {

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{\textquoteright}s building energy simulation leads to trustworthy energy use predictions.

}, url = {https://www.ashrae.org/File\%20Library/Conferences/Specialty\%20Conferences/2018\%20Building\%20Performance\%20Analysis\%20Conference\%20and\%20SimBuild/Papers/C107.pdf}, author = {Michael Wetter and Jianjun Hu and Milica Grahovac and Brent Eubanks and Philip Haves} } @article {30029, title = {Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs}, year = {2017}, abstract = {

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{\textquoteright}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.

}, keywords = {Behavior Modeling, building performance simulation, co-simulation, data model, occupant behavior}, author = {Tianzhen Hong and Yixing Chen and Zsofia Belafi and Simona D{\textquoteright}Oca} } @article {30316, title = {Occupancy data analytics and prediction: a case study}, year = {2016}, abstract = {

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.

}, keywords = {data mining, Machine learning, occupancy prediction, occupant presence}, author = {Xin Liang and Tianzhen Hong and Geoffrey Qiping Shen} } @article {59987, title = {Occupancy Schedules Learning Process Through a Data Mining Framework}, journal = {Energy and Buildings}, volume = {88}, year = {2015}, month = {02/2015}, pages = {395-408}, abstract = {

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.

}, keywords = {Behavioral Pattern, building simulation, data mining, Occupancy schedule, occupant behavior, Office Building}, doi = {10.1016/j.enbuild.2014.11.065}, author = {Simona D{\textquoteright}Oca and Tianzhen Hong} } @article {60948, title = {Occupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges}, journal = {Energy and Buildings}, volume = {107}, year = {2015}, month = {11/2015}, pages = {264-278}, abstract = {

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.

}, keywords = {building simulation, energy efficiency, energy modeling, energy use, occupant behavior}, doi = {10.1016/j.enbuild.2015.08.032}, author = {Da Yan and William O{\textquoteright}Brien and Tianzhen Hong and Xiaohang Feng and H. Burak Gunay and Farhang Tahmasebi and Ardeshir Mahdavi} } @article {59959, title = {An Ontology to Represent Energy-related Occupant Behavior in Buildings Part I: Introduction to the DNAs Framework}, journal = {Building and Environment}, volume = {92}, year = {2015}, month = {10/2015}, pages = {764-777}, abstract = {

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.

}, keywords = {Building energy, human-building-system interaction, modeling, occupant behavior, ontology, simulation}, doi = {10.1016/j.buildenv.2015.02.019}, author = {Tianzhen Hong and Simona D{\textquoteright}Oca and William J. N. Turner and Sarah C. Taylor-Lange} } @article {60959, title = {An Ontology to Represent Energy-Related Occupant Behavior in Buildings. Part II: Implementation of the DNAS framework using an XML schema}, journal = {Building and Environment}, volume = {94}, year = {2015}, month = {08/2015}, pages = {196-205}, abstract = {

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 {\textquoteleft}occupant behavior XML{\textquoteright} (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.

}, keywords = {building energy consumption, building simulation, energy modeling, obXML, occupant behavior, XML schema}, doi = {10.1016/j.buildenv.2015.08.006}, author = {Tianzhen Hong and Simona D{\textquoteright}Oca and Sarah C. Taylor-Lange and William J. N. Turner and Yixing Chen and Stefano P. Corgnati} } @proceedings {227, title = {Object-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 system}, journal = {8th Nordic Symposium on Building Physics in the Nordic Countries 2008}, volume = {2}, year = {2008}, pages = {pp.825-832}, address = {Copenhagen, Denmark}, abstract = {

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{\textregistered} 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.

}, issn = {978-87-7877-265-7}, author = {Thierry Stephane Nouidui and Christoph Nytsch-Geusen and Andreas Holm and Klaus Sedlbauer} } @proceedings {245, title = {Overcoming the Complexity of Diagnostic Problems due to Sensor Network Architecture}, journal = {Eleventh International Conference on Intelligent Systems and Controls}, year = {2008}, month = {11/08}, abstract = {

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

}, url = {https://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=503}, author = {Massieh Najafi and David M. Auslander and Peter L. Bartlett and Philip Haves} } @article {424, title = {Outdoor synthetic temperature for the calculation of space heating load}, journal = {Energy and Buildings}, volume = {28}, year = {1998}, month = {1998}, pages = {269-277}, type = {Research Article}, abstract = {

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.

}, keywords = {heating load calculation, outdoor design conditions, residential buildings, stochastic analysis, thermal performance of buildings}, doi = {10.1016/S0378-7788(98)00013-9}, author = {Tianzhen Hong and Yi Jiang} } @conference {55946, title = {Optimal Cogeneration Systems for High-Rise Office Buildings}, booktitle = {18th Intersociety Energy Conversion Engineering Conference }, year = {1983}, month = {05/1983}, address = {Orlando, FL}, author = {Joseph H. Eto} } @proceedings {369, title = {One Dimensional Representations of the Heat Flow Beneath a Building}, journal = {US DOE Solar Energy Storage Options Conference}, year = {1979}, month = {03/1979}, address = {San Antonio, TX}, author = {Philip Haves} } @article {371, title = {The Orientation of the Magnetic Field in Radio Sources}, journal = {Monthly Notices of the Royal Astronomical Society}, volume = {173}, year = {1975}, month = {11/1975}, pages = {53P-56P}, chapter = {53P}, abstract = {

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.

}, keywords = {extragalactic radio sources, magnetic field configurations, polarization (waves), polarization characteristics, polarized electromagnetic radiation, radiant flux density, radio galaxies}, author = {Philip Haves and Robin G. Conway} }