Intelligent Residential Energy Management System Using Deep Reinforcement Learning

The rising demand for electricity and its essential nature in today's world call for intelligent home energy management systems that can reduce energy usage. This article aims a novel way to develop a learning system that can learn from experience to shift loads from one time instance to another and achieve the goal of minimizing the aggregate peak load. Specifically, this article proposes a deep reinforcement learning model for demand response where the virtual agent learns the task like humans learns a task. The agent gets feedback for every action it takes in the environment; these feedbacks will drive the agent to learn about the environment and take much smarter steps later in its learning stages. The proposed approach outperformed the state of the art mixed integer linear programming for load peak reduction. Other key contribution is the design of an agent to minimize both consumer electricity bills and system peak load demand simultaneously. The proposed model was analyzed with loads from five different residential consumers; the method increases the monthly savings of each consumer by reducing their electricity bill drastically along with minimizing the peak load of the grid.