Intelligent Residential Energy Management System using Deep Reinforcement Learning

TL;DR

This paper presents a Deep Reinforcement Learning-based residential energy management system that learns to shift loads, reducing peak demand and electricity bills more effectively than traditional methods.

Contribution

It introduces a novel DRL model for demand response that outperforms existing MILP approaches in peak load reduction and cost savings.

Findings

DRL model reduces peak load more effectively than MILP.

The system increases monthly savings for residential consumers.

Simultaneously minimizes consumer bills and system peak load.

Abstract

The rising demand for electricity and its essential nature in today's world calls for intelligent home energy management (HEM) systems that can reduce energy usage. This involves scheduling of loads from peak hours of the day when energy consumption is at its highest to leaner off-peak periods of the day when energy consumption is relatively lower thereby reducing the system's peak load demand, which would consequently result in lesser energy bills, and improved load demand profile. This work introduces 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. This paper proposes a Deep Reinforcement Learning (DRL) model for demand response where the virtual agent learns the task like humans do. 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. Our method outperformed the state of the art mixed integer linear programming (MILP) for load peak reduction. The authors have also designed an agent to learn to minimize both consumers' electricity bills and utilities' system peak load demand simultaneously. The proposed model was analyzed with loads from five different residential consumers; the proposed method increases the monthly savings of each consumer by reducing their electricity bill drastically along with minimizing the peak load on the system when time shiftable loads are handled by the proposed method.