Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Using an energy disaggregation method, the consumption of individual appliances can be estimated from the aggregate measurement. Recent disaggregation algorithms have significantly improved the performance of NILM systems. However, the generalization capability of these methods to different houses as well as the disaggregation of multi-state appliances are still major challenges. In this paper we address these issues and propose an energy disaggregation approach based on the variational autoencoders framework. The probabilistic encoder makes this approach an efﬁcient model for encoding information relevant to the reconstruction of the target appliance consumption. In particular, the proposed model accurately generates more complex load proﬁles, thus improving the power signal reconstruction of multi-state appliances. Moreover, its regularized latent space improves the generalization capabilities of the model across different houses. The proposed model is compared to state-of-the-art NILM approaches on the UK-DALE and REFIT datasets, and yields competitive results. The mean absolute error reduces by 18% on average across all appliances compared to the state-of-the-art. The F1-Score increases by more than 11%, showing improvements for the detection of the target appliance in the aggregate measurement.
Energy disaggregation using variational autoencoders
About the Author: antoine langevin
Antoine Langevin received his BSc in Electrical Engineering from the École de Technologie Supérieure, Montreal in 2016. He then started graduate studies by joining the Synchromedia laboratory of the École de Technologie Supérieure. His current research project is on energy disaggregation using machine learning techniques. He also participates in the Smart ÉTS Residence project. His areas of interest in research are artiﬁcial intelligence, smart home/city, prototyping and embedded systems.