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

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dc.creator Avcı, Ali Berkay
dc.date 2025-01-25T00:00:00Z
dc.date.accessioned 2025-02-25T10:20:07Z
dc.date.available 2025-02-25T10:20:07Z
dc.identifier 39dcad96-b836-4118-9b67-c92c961351d6
dc.identifier 10.5281/zenodo.14738857
dc.identifier https://avesis.sdu.edu.tr/publication/details/39dcad96-b836-4118-9b67-c92c961351d6/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/99362
dc.description <p><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;">ABSTRACT</span><br style="box-sizing: inherit; font-family: Helvetica, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, 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, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;">technologies that prioritize both sustainability and human well-being.</span><br style="box-sizing: inherit; font-family: Helvetica, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;"><span style="font-family: Helvetica, &quot;Helvetica Neue&quot;, Arial, sans-serif; font-size: 14px;">Keywords: Machine Learning, Thermal Comfort, Energy Optimization, Smart Buildings&nbsp;</span></p>
dc.language eng
dc.rights info:eu-repo/semantics/openAccess
dc.title AI AND DYNAMIC THERMAL COMFORT CONTROL: A SYNTHESIS OF MACHINE LEARNING-BASED APPROACHES FOR ENERGY OPTIMIZATION
dc.type info:eu-repo/semantics/conferenceObject


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