Optimizing Energy Efficiency in Smart Buildings Through Intelligent HVAC Control Systems

This work explores the optimization of HVAC systems in smart buildings through the integration of advanced methodologies, including machine learning algorithms. Specif-ically, we investigate the application of neural networks, and genetic algorithms to enhance energy efficiency and user comfort. Through a comprehensive experimental evaluation, we validated of our proposed approach over existing optimization strategies, as evidenced by improvements in predictive accuracy, user satisfaction, and energy consumption reduction. Our findings highlight the efficacy of leveraging advanced optimization techniques in addressing the complex challenges of HVAC control in modern building infrastructures.