Estimating Buildings’ Parameters over Time Including Prior Knowledge

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.