Towards sustainable energy use: Reinforcement learning for demand response in commercial buildings

Demand response (DR) is a crucial strategy for balancing electricity demand and reducing environmental impact, especially as power consumption continues to rise. Small and medium-sized commercial buildings hold significant potential for DR implementation due to their widespread presence and substantial contribution to overall energy use. This study proposes a novel framework to optimize energy management in these buildings by considering three types of loads: non-controllable, controllable with discrete action spaces (HVAC), and controllable with continuous action spaces (lighting). The objective is to minimize costs, reduce CO 2 emissions, improve occupant comfort, and shave peak loads as a unified goal. State-of-the-art reinforcement learning (RL) algorithms are employed and compared against traditional heuristic methods using real-world data. Results show that RL-based approaches can significantly lower energy costs and environmental impacts while maintaining occupant comfort, even under varying outdoor temperature conditions. By incorporating risk assessments through Value at Risk (VaR) and Conditional Value at Risk (CVaR) metrics, this study offers a robust solution for sustainable energy management, providing insights for policymakers and industry practitioners aiming for a more resilient energy future.