A Bayesian Data Analytics Approach to Buildings’ Thermal Parameter Estimation

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dc.contributor.authorPathak, Nilavra
dc.contributor.authorFoulds, James
dc.contributor.authorRoy, Nirmalya
dc.contributor.authorBanerjee, Nilanjan
dc.contributor.authorRobucci, Ryan
dc.date.accessioned2019-11-14T17:26:19Z
dc.date.available2019-11-14T17:26:19Z
dc.date.issued2019-06-28
dc.descriptione-EnergyEnergy-Efficient Computing and Networking, In Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy ’19), June 25–28, 2019, Phoenix, AZ, USA. ACM, New York, NY, USAen_US
dc.description.abstractModeling buildings’ heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents’ behavior. Gray-box models offer an explanation of those dynamics, as expressed in a few parameters specific to built environments that can provide compelling insights into the characteristics of building artifacts. In this paper, we present a systematic study of Bayesian approaches to modeling buildings’ parameters, and hence their thermal characteristics. We build a Bayesian state-space model that can adapt and incorporate buildings’ thermal equations and postulate a generalized solution that can easily adapt prior knowledge regarding the parameters. We then show that a faster approximate approach using Variational Inference for parameter estimation can posit similar parameters’ quantification as that of a more time-consuming Markov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on two datasets to understand the generative process and attest that the Bayesian approach is more interpretable. We further study the effects of prior selection on the model parameters and transfer learning, where we learn parameters from one season and reuse them to fit the model in other seasons. We perform extensive evaluations on controlled and real data traces to enumerate buildings’ parameters within a 95% credible interval.en_US
dc.description.sponsorshipThis work is supported by the NSF CPS award #1544687.en_US
dc.description.urihttps://dl.acm.org/citation.cfm?id=3328316en_US
dc.format.extent11 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2mzu2-mymg
dc.identifier.citationNilavra Pathak, James Foulds, Nirmalya Roy, Nilanjan Banerjee, and Ryan Robucci. 2019. A Bayesian Data Analytics Approach to Buildings’ Thermal Parameter Estimation. In Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy ’19), June 25–28, 2019, Phoenix, AZ, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3307772. 3328316en_US
dc.identifier.urihttps://doi.org/10.1145/3307772.3328316
dc.identifier.urihttp://hdl.handle.net/11603/16299
dc.language.isoen_USen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectbuilding parameter estimationen_US
dc.subjectstate space modelsen_US
dc.subjectbayesian inferenceen_US
dc.subjectcomputing methodologiesen_US
dc.subjectlatent variable modelsen_US
dc.subjectmathematics of computingen_US
dc.subjectbayesian computationen_US
dc.subjectapplied computingen_US
dc.subjectphysicsen_US
dc.titleA Bayesian Data Analytics Approach to Buildings’ Thermal Parameter Estimationen_US
dc.typeTexten_US

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