Neural Network-Aided NILM (NNAN) disaggregation: Revealing appliance consumption patterns with iterative subtraction

Abstract

Non-Intrusive Load Monitoring (NILM) is a method to decompose overall electricity consumption into individual appliance-level data, utilizing the primary meter’s readings without additional sensors on each device. This article introduces a novel approach which is a Neural Network-Aided NILM (NNAN), focusing on revealing appliance consumption patterns by following a sequential subtraction method. Our goal is to tackle the issue where high-power and highly-used appliances make it difficult for neural networks to accurately separate the usage of lower-power and less-used appliances. We mainly employ Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) using inception blocks as key components. Our proposed architecture is validated on three public datasets that are AMPds2, ECO and UK-DALE. The NNAN model showed promising results, achieving disaggregation accuracy improvements of up to 5.13% on AMPds2, 3.79% on ECO, and 9.55% on UK-DALE compared to the reference methods. Additionally, NNAN reduces model complexity, requiring up to 74% fewer parameters than traditional deep learning approaches, leading to improved computational efficiency. Finally, NNAN demonstrated a reduced correlation between appliance usage rates and disaggregation accuracies.