AIoT-Empowered Smart Grid Energy Management with Distributed Control and Non-Intrusive Load Monitoring

Linfeng Shen, Feng Wang, Miao Zhang, Jiangchuan Liu, Gaoyang Liu, Xiaoyi Fan

Abstract: Today's electrical grid is experiencing a fast transition toward a smart infrastructure. Modern smart grid is expected to integrate Artificial Intelligence of Things (AIoT)-empowered energy management systems (EMS) to sense, analyze, and optimize the power consumption and QoS of diverse end users. Non-Intrusive Load Monitoring (NILM) plays a key role in this transition, particularly considering that many legacy devices/appliances may not have built-in sensors. Yet most of the NILM solutions rely on large (often impractical) datasets for training. In this paper, we address this challenge through a meta learning-inspired approach, which implements a hierarchical architecture with a “meta-learner” to supervise the training of each appliance. Current EMS also relies on a central controller to access long-term information across all participants, which mismatches their distributed nature, and so often with slow responses. To this end, we develop a deep reinforcement learning based controller to make dynamic decisions for each component in the system. The experiment results based on real-world data sets and simulation data show that applying the meta learning approach can greatly improve the performance of NILM and the QoS of the whole system.