Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.

Keywords: energy disaggregation; non-intrusive load monitoring; NILM; Bayesian methods

Cite this article: Massidda, L.; Marrocu, M. A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation. Sensors 2022, 22, 4481. https://doi.org/10.3390/s22124481 

About the authors

Luca Massidda

Group leader of the Smart Energy Systems team at CRS4
– HPC for Energy and Environment



Marino Marrocu

Senior researcher
Head of the Environmental Sciences branch of the Energy & Environment program at CRS4

– HPC for Energy and Environment