Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation

Abstract Non-Intrusive Load Monitoring (NILM) is the task of determining the appliances individual contributions to the aggregate power consumption by using a set of electrical parameters measured at a single metering point. NILM allows to provide detailed consumption information to the users, that induces them to modify their habits towards a wiser use of the electrical energy. This paper proposes a NILM algorithm based on the Deep Neural Networks. In particular, the NILM task is treated as a noise reduction problem addressed by using denoising autoencoder (dAE) architecture, i.e., a neural network trained to reconstruct a signal from its noisy version. This architecture has been initially proposed by Kelly and Knottenbelt (2015), and here is extended and improved by conducting a detailed study on the topology of the network, and by intelligently recombining the disaggregated output with a median filter. An additional contribution of this paper is an exhaustive comparative evaluation conducted with respect to one of the reference work in the field of Hidden Markov Models (HMM) for NILM, i.e., the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm. The experiments have been conducted on the AMPds, UK-DALE, and REDD datasets in seen and unseen scenarios both in presence and in absence of noise. In order to be able to evaluate AFAMAP in presence of noise, an HMM model representing the noise contribution has been introduced. The results showed that the dAE approach outperforms the AFAMAP algorithm both in seen and unseen condition, and that it exhibits a significant robustness in presence of noise.