Enhancing Grid Stability through Grid-Interactive Efficient Buildings with Deep Reinforcement Learning: Innovations and Challenges

Abstract

Integrating Deep Reinforcement Learning (DRL) into building energy management systems presents a transformative approach to enhancing grid stability and efficiency. Grid-Interactive Efficient Buildings (GEBs), equipped with advanced DRL algorithms, can dynamically optimize their energy consumption and production in response to real-time grid conditions. This paper explores the innovative applications of DRL in GEBs, highlighting its potential to autonomously optimize energy decisions, accommodate the stochastic nature of renewable energy sources, and effectively respond to variable building energy demands. Through a comprehensive analysis, this study not only sheds light on the successes to date but also maps out the significant challenges that must be overcome. By addressing these challenges, DRL for building energy management can fully realize its potential, leading to a more sustainable and efficient energy future.