Deep reinforcement learning-based energy scheduling for green buildings with stationary and EV batteries of heterogeneous characteristics

Puntos clave

• Energy scheduling improves significantly with deep reinforcement learning strategies, leading to better utility use.
• The analysis showed a potential reduction in energy costs by 30% when utilizing both stationary and EV batteries.
• Analysis using advanced algorithms compared heterogeneous battery types under real-world conditions for optimization.
• The findings highlight the need for adaptive energy management in green buildings to enhance sustainability and efficiency.