AI AND DYNAMIC THERMAL COMFORT CONTROL: A SYNTHESIS OF MACHINE LEARNING-BASED APPROACHES FOR ENERGY OPTIMIZATION
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2025
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| _version_ | 1866901839832678400 |
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| author | AVCI, Ali Berkay |
| author_facet | AVCI, Ali Berkay |
| contents | <p>ABSTRACT<br>Advancements in machine learning have revolutionized various industries, including building<br>energy management and thermal comfort optimization. The integration of these technologies<br>offers transformative potential for developing intelligent, adaptive systems in the built<br>environment. This paper provides a comprehensive review of machine learning-based<br>approaches in dynamic thermal comfort control systems, focusing on their potential for<br>energy optimization in various building typologies. As HVAC systems evolve to balance<br>thermal comfort with energy efficiency, machine learning algorithms such as artificial neural<br>networks, fuzzy logic, and reinforcement learning are increasingly being applied to predict<br>and adjust environmental settings dynamically. By analyzing key studies in the field, this<br>review identifies the advantages and limitations of different machine learning models in<br>terms of energy savings and occupant comfort. The paper also highlights the gaps in current<br>research, particularly the need for more real-time, adaptive models that can integrate both<br>occupant behavior and external environmental factors. The findings suggest that machine<br>learning offers significant potential for reducing energy consumption in buildings while<br>maintaining or improving thermal comfort, but further development is necessary to refine<br>these systems for broader and more reliable applications. Ultimately, this review aims to<br>serve as a foundation for future research, fostering advancements in smart building<br>technologies that prioritize both sustainability and human well-being.<br>Keywords: Machine Learning, Thermal Comfort, Energy Optimization, Smart Buildings </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14738857 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI AND DYNAMIC THERMAL COMFORT CONTROL: A SYNTHESIS OF MACHINE LEARNING-BASED APPROACHES FOR ENERGY OPTIMIZATION AVCI, Ali Berkay machine learning thermal comfort energy optimization smart buildings <p>ABSTRACT<br>Advancements in machine learning have revolutionized various industries, including building<br>energy management and thermal comfort optimization. The integration of these technologies<br>offers transformative potential for developing intelligent, adaptive systems in the built<br>environment. This paper provides a comprehensive review of machine learning-based<br>approaches in dynamic thermal comfort control systems, focusing on their potential for<br>energy optimization in various building typologies. As HVAC systems evolve to balance<br>thermal comfort with energy efficiency, machine learning algorithms such as artificial neural<br>networks, fuzzy logic, and reinforcement learning are increasingly being applied to predict<br>and adjust environmental settings dynamically. By analyzing key studies in the field, this<br>review identifies the advantages and limitations of different machine learning models in<br>terms of energy savings and occupant comfort. The paper also highlights the gaps in current<br>research, particularly the need for more real-time, adaptive models that can integrate both<br>occupant behavior and external environmental factors. The findings suggest that machine<br>learning offers significant potential for reducing energy consumption in buildings while<br>maintaining or improving thermal comfort, but further development is necessary to refine<br>these systems for broader and more reliable applications. Ultimately, this review aims to<br>serve as a foundation for future research, fostering advancements in smart building<br>technologies that prioritize both sustainability and human well-being.<br>Keywords: Machine Learning, Thermal Comfort, Energy Optimization, Smart Buildings </p> |
| title | AI AND DYNAMIC THERMAL COMFORT CONTROL: A SYNTHESIS OF MACHINE LEARNING-BASED APPROACHES FOR ENERGY OPTIMIZATION |
| topic | machine learning thermal comfort energy optimization smart buildings |
| url | https://doi.org/10.5281/zenodo.14738857 |