Enhancing Thermal Comfort and Energy Efficiency in Buildings Using Artificial Intelligence: A Systematic Literature Review

Abstract

Improving thermal comfort in buildings is essential for enhancing occupant satisfaction and well being, but it can often lead to higher energy consumption. Adjusting heating, ventilation, and air conditioning (HVAC) systems, optimizing airflow, or maintaining consistent temperatures can increase energy use.

Recent advancements in Artificial Intelligence (AI) offer the ability to manage both thermal comfort and energy efficiency simultaneously without sacrificing one for the other. This study utilizes a Systematic Literature Review (SLR) of 230 studies to explore AI's potential in improving thermal comfort and energy efficiency. The findings highlight six areas where AI outperforms traditional methods: (1) thermal comfort prediction, (2) personalized thermal comfort models, (3) occupancy detection and behavior prediction, (4) building design for comfort and efficiency, (5) fault detection and system diagnostics, and (6) occupant health and integration with other indoor environmental quality (IEQ) factors. This study highlights several examples of AI's potential and suggests future research directions to fully harness these opportunities.