A Smart Home Energy Management System as an Intelligent Electricity Energy Audit Based on AI-empowered Non-Intrusive Load Monitoring

Abstract

Electrical energy demands requested from downstream sectors of a smart grid are continuously increasing. One way to meet those demands is to monitor and manage industrial, commercial as well as residential electrical appliances efficiently in response to demand response programs. This study aims to develop a smart Home Energy Management System (HEMS) that acts as an intelligent electricity energy audit based on Non-Intrusive Load Monitoring (NILM) technology. NILM instead of HEMS conducted as a benchmark in a field of interest is able to infer appliance-level power consumption without an intrusive deployment of smart e-meters installed and attached on monitored individual electrical appliances. To NILM as a load classification task, a Radial Basis Function-Artificial Neural Network (RBF-ANN) hybridized with k-Means clustering is developed and used to identify individual electrical appliances monitored in a realistic residential environment. The experimentation reported in this study shows that, the presented HEMS utilizing the proposed k-Means clustering-hybridized RBF-ANN-based NILM as an intelligent electricity energy audit gave an overall load classification rate of 72.57%.