AI AND DYNAMIC THERMAL COMFORT CONTROL: A SYNTHESIS OF MACHINE LEARNING-BASED APPROACHES FOR ENERGY OPTIMIZATION

Abstract

ABSTRACT Advancements in machine learning have revolutionized various industries, including building energy management and thermal comfort optimization. The integration of these technologies offers transformative potential for developing intelligent, adaptive systems in the built environment. This paper provides a comprehensive review of machine learning-based approaches in dynamic thermal comfort control systems, focusing on their potential for energy optimization in various building typologies. As HVAC systems evolve to balance thermal comfort with energy efficiency, machine learning algorithms such as artificial neural networks, fuzzy logic, and reinforcement learning are increasingly being applied to predict and adjust environmental settings dynamically. By analyzing key studies in the field, this review identifies the advantages and limitations of different machine learning models in terms of energy savings and occupant comfort. The paper also highlights the gaps in current research, particularly the need for more real-time, adaptive models that can integrate both occupant behavior and external environmental factors. The findings suggest that machine learning offers significant potential for reducing energy consumption in buildings while maintaining or improving thermal comfort, but further development is necessary to refine these systems for broader and more reliable applications. Ultimately, this review aims to serve as a foundation for future research, fostering advancements in smart building technologies that prioritize both sustainability and human well-being. Keywords: Machine Learning, Thermal Comfort, Energy Optimization, Smart Buildings