# Bayesian model of electrical heating disaggregation

arXiv: https://arxiv.org/abs/2011.05674 · HTML: https://mlsanity.com/paper/2011.05674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater](https://mlsanity.com/paper/2203.11268/) (2022)
- [Occupancy Detection Based on Electricity Consumption](https://mlsanity.com/paper/2312.08535/) (2023)

## Abstract (arXiv)

Adoption of smart meters is a major milestone on the path of European transition to smart energy. The residential sector in France represents ≈35% of electricity consumption with ≈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.