Design and implementation of an indoor environment management system using a deep reinforcement learning approach

Abstract

The Indoor Household Environment (IHE) has caught the attention of industry and academia due to the substantial amount of time that families spend indoors. Domestic air pollution imposes significant burdens on communities, although both indoor and outdoor sources of air pollution contribute to outdoor pollution. This article presents a precise approach for an Intelligent Home Energy Management System (IHEMS) that integrates Deep Reinforcement Learning (DRL) and Demand Response (DR) techniques. IHEMS employs Markov decision processes to handle problems and concerns efficiently. A novel approach has been developed to enhance the efficiency of deep reinforcement learning control, utilizing a double deep Q network and a restart strategy during a priority event. Unlike the HEMS technique, which uses a predictive control model, the optimal strategy resulted in a 20% decrease in monthly power consumption.