Integrating non-intrusive load monitoring based on graph-to-point learning into a self-adaptive home energy management system

Abstract

Non-intrusive load monitoring (NILM) can extract the detailed consumption of target appliances from mains reading of a building, which is of great significance for consumers to optimize their electricity consumption and participate in demand response programs. Due to the fact that appliances with multiple states and complex working patterns often bring the most challenging obstacles in energy disaggregation, conventional deep learning methods tend to lead to unsatisfactory disaggregation results. Hence, a novel graph-to-point learning is proposed, in which residual graph convolutional network (ResGCN) and dual attention bidirectional long short-term memory (DABiLSTM) are combined to inherently capture the characteristics of time dependency. Then, accurate NILM results are integrated into a user-centric and self-adaptive home energy management system (HEMS), where a discrete Bayesian network for each appliance is adopted to automatically describe the end-users’ consumption behaviors. Lastly, the proposed algorithm is compared with state-of-the-art methods based on REFIT and REDD datasets for performance validation. Experiments results show that compared with traditional HEMS, NILM-based HEMS can markedly reduce operation cost by 53% and improve the overall consumer comfort by 31%.