http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/16814 Mimarlık Fakültesine ait bölümleri içerir. Mon, 20 Apr 2026 11:50:08 GMT 2026-04-20T11:50:08Z http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/98803 AI AND DYNAMIC THERMAL COMFORT CONTROL: A SYNTHESIS OF MACHINE LEARNING-BASED APPROACHES FOR ENERGY OPTIMIZATION AVCI, Ali Berkay 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 Sat, 25 Jan 2025 00:00:00 GMT http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/98803 2025-01-25T00:00:00Z http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/98802 AI-Driven Approaches to Enhance Energy Efficiency in Heritage Architecture: A Review AVCI, Ali Berkay This review explores the role of artificial intelligence (AI) in enhancing energy efficiency within heritage buildings, focusing on balancing sustainability goals with the preservation of historical and architectural integrity. AI technologies such as Building Energy anagement Systems (BEMS), digital twins, and reinforcement learning provide innovative solutions to optimize energy use while minimizing physical interventions. Heritage buildings pose unique challenges for energy retrofits due to structural and regulatory constraints, but AI-driven tools offer non-invasive strategies that align with conservation principles. By predicting energy consumption patterns, facilitating adaptive climate control, and improving predictive maintenance, AI technologies can ensure that energy efficiency goals are met without compromising the building’s historical character. The review also addresses ethical considerations, such as data privacy and the cultural implications of AI interventions in heritage structures. This study highlights the potential for AI to revolutionize energy retrofitting in heritage architecture, providing a roadmap for future research on the integration of AI with sustainable building practices. Tue, 19 Nov 2024 00:00:00 GMT http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/98802 2024-11-19T00:00:00Z