Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring

Abstract

Non-intrusive load monitoring (NILM) is defined as the task of retrieving the active power consumption of two or more appliances from information gathered at a single metering point. In this work, the use of the reactive aggregate power as an additional feature to the commonly used active power for deep neural models is proposed. The NILM problem is formulated as a denoising problem, and denoising autoencoder (dAE) neural architectures are used to estimate the appliances individual active power consumption. The proposed approach is evaluated on two public datasets: the Almanac of Minutely Power dataset (AMPds) and the UK Domestic Appliance-Level Electricity (UK-DALE) dataset. In order to better evaluate the generalization capabilities of the algorithm, different testing conditions are considered for the UK-DALE dataset, namely a seen and an unseen scenario. The results show that introducing the reactive power can indeed bring and overall performance increase in all scenarios, ranging from +4.9% to +8.4% of the energy-based F1 score.