Hidden Markov Models revealing the household thermal profiling from smart meter data

This work describes a methodology based on Hidden Markov Models (HMMs) that are applied for revealing household thermal load profiles which are not available to direct observation. This research is motivated by the necessity of reducing the energy consumption for cooling and heating in residential buildings. Our methodology uses data that is becoming readily available at households - hourly energy consumption records collected from smart electricity meters, as well as hourly outdoor air temperature records. The heat transfer regime, namely the states corresponding to lower or higher building hourly thermal loads related to the outdoor air temperatures, will be considered as the underlying mechanism affecting the generation of observations. We aggregate the observed data to obtain a certain number of clusters. The problem of HMM estimation is addressed and the subsequent HMMs are compared on the basis of information criteria, like Akaike and Bayesian Information Criteria. Our goal is to reveal the dynamic of building thermal load (heating/cooling) under the uncertainties induced by the residents' behavior. Consequently, we present examples of thermal load profiles generated using our best HMM on a testing facility located in the Polytechnic University of Bucharest campus, namely the UPB's passive building house.