Description:
<p><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">ABSTRACT</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">Advancements in machine learning have revolutionized various industries, including building</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">energy management and thermal comfort optimization. The integration of these technologies</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">offers transformative potential for developing intelligent, adaptive systems in the built</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">environment. This paper provides a comprehensive review of machine learning-based</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">approaches in dynamic thermal comfort control systems, focusing on their potential for</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">energy optimization in various building typologies. As HVAC systems evolve to balance</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">thermal comfort with energy efficiency, machine learning algorithms such as artificial neural</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">networks, fuzzy logic, and reinforcement learning are increasingly being applied to predict</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">and adjust environmental settings dynamically. By analyzing key studies in the field, this</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">review identifies the advantages and limitations of different machine learning models in</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">terms of energy savings and occupant comfort. The paper also highlights the gaps in current</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">research, particularly the need for more real-time, adaptive models that can integrate both</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">occupant behavior and external environmental factors. The findings suggest that machine</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">learning offers significant potential for reducing energy consumption in buildings while</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">maintaining or improving thermal comfort, but further development is necessary to refine</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">these systems for broader and more reliable applications. Ultimately, this review aims to</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">serve as a foundation for future research, fostering advancements in smart building</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">technologies that prioritize both sustainability and human well-being.</span><br style="box-sizing: inherit; font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, "Helvetica Neue", Arial, sans-serif; font-size: 14px;">Keywords: Machine Learning, Thermal Comfort, Energy Optimization, Smart Buildings </span></p>