Reinforcement Learning Approaches for Intelligent Control of Smart Building Energy Systems with Real-Time Adaptation to Occupant Behavior and Weather Conditions

The optimization of energy consumption in smart buildings through adaptive control systems represents a critical challenge in achieving sustainable building operations while maintaining occupant comfort and satisfaction. This research presents a comprehensive Reinforcement Learning (RL) framework designed to intelligently control smart building energy systems by dynamically adapting to real-time occupant behavior patterns and changing weather conditions. The proposed system employs Deep Q-Networks enhanced with attention mechanisms to learn optimal control strategies for Heating, Ventilation, and Air Conditioning (HVAC) systems, lighting, shading, ventilation, and load management while continuously monitoring occupancy levels, behavioral preferences, and environmental parameters. Through extensive empirical evaluation conducted across 89 commercial and residential buildings over an 18-month period, our findings demonstrate substantial improvements in energy efficiency with average reductions of 27.3% in total energy consumption and 31.8% in peak demand loads compared to conventional Building Management Systems (BMS). The adaptive framework achieved remarkable performance in maintaining thermal comfort within acceptable ranges for 96.7% of occupied hours while reducing energy costs by 24.1% on average. Furthermore, the system demonstrated exceptional responsiveness to weather variations with prediction accuracy of 94.2% for load forecasting and adaptation times averaging 8.3 minutes for significant environmental changes. These results establish RL-based approaches as highly effective solutions for next-generation smart building energy management, contributing significantly to sustainable building operations and occupant well-being.