Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
Researchers have developed an explainable deep reinforcement learning (XRL) framework to optimize energy management in residential buildings. This approach addresses the 'black-box' nature of traditional deep reinforcement learning, enhancing user trust and practical adoption. The framework was tested using both synthetic and real-world data, demonstrating its ability to reduce electricity costs through intelligent battery management while providing transparent insights into the decision-making process. AI
IMPACT Enhances trust and adoption of AI in energy management by providing transparent decision-making.