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    Estimating Buildings’ Parameters over Time Including Prior Knowledge

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    1901.07469.pdf (1.330Mb)
    Links to Files
    https://arxiv.org/pdf/1901.07469.pdf
    Permanent Link
    http://hdl.handle.net/11603/12773
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    • UMBC Faculty Collection
    • UMBC Information Systems Department
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    Author/Creator
    Pathak, Nilavra
    Foulds, James
    Roy, Nirmalya
    Banerjee, Nilanjan
    Robucci, Ryan
    Date
    2019-02-04
    Type of Work
    11 pages
    Text
    journal article preprints
    Rights
    This 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.
    Subjects
    building parameter identification
    grey box modeling
    state space models
    bayesian estimation
    Abstract
    Modeling buildings’ heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents’ behavior. Graybox models offer an explanation of those dynamics, as expressed in a few parameters specific to built environments. These parameters can provide compelling insights into the characteristics of building artifacts and have various applications such as forecasting HVAC usage, indoor temperature control monitoring of built environments, and more. In this paper, we present a systematic study of Bayesian approaches to modeling buildings’ parameters, and hence their thermal characteristics. We build a Bayesian statespace 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.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.