Bayesian model of electrical heating disaggregation

TL;DR

This paper introduces a Bayesian model for disaggregating electrical heating consumption from smart meter data, leveraging temperature data and household metadata to improve energy analysis in residential settings.

Contribution

It presents a novel Bayesian mixture model that disaggregates electrical heating from overall consumption using temperature and household metadata, in an unsupervised manner.

Findings

Model effectively separates heating from other electrical loads.

Disaggregation accuracy improves with household metadata.

Model applicable to large-scale smart meter datasets.

Abstract

Adoption of smart meters is a major milestone on the path of European transition to smart energy. The residential sector in France represents $\approx$35\% of electricity consumption with $\approx$40\% (INSEE) of households using electrical heating. The number of deployed smart meters Linky is expected to reach 35M in 2021. In this manuscript we present an analysis of 676 households with an observation period of at least 6 months, for which we have metadata, such as the year of construction and the type of heating and propose a Bayesian model of the electrical consumption conditioned on temperature that allows to disaggregate the heating component from the electrical load curve in an unsupervised manner. In essence the model is a mixture of piece-wise linear models, characterised by a temperature threshold, below which we allow a mixture of two modes to represent the latent state home/away.