Bayesian model of electrical heating disaggregation
This work addresses energy management for households and utilities in France, where electrical heating is common, but it is incremental as it builds on existing disaggregation methods.
The authors tackled the problem of disaggregating electrical heating consumption from household load curves using a Bayesian model conditioned on temperature, achieving unsupervised separation of heating components from smart meter data.
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.