Estimating Buildings’ Parameters over Time Including Prior Knowledge
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2019-02-04
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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.