A systematic analysis of AI-based methods for thermal control and energy-efficiency in Sustainable Buildings (Architecture Image Studies, 2025)
Construction activities account for a substantial proportion of total energy consumption in many nations, attributable to the widespread adoption of Heating, Ventilation, and Air-Conditioning (HVAC) systems driven by the increasing demand for enhanced thermal convenience. Minimizing energy usage while preserving pleasant environments in structures presents conflicting aims and constitutes a classic optimization challenge for intelligent system design. In the past decade, many strategies utilizing Artificial Intelligence (AI) technologies have been implemented to optimize energy consumption in HVAC devices while ensuring appropriate consumer temperature control. The article conducts a thorough systematic analysis of AI-based models employed in building automation by evaluating their results, the applications in the analyzed studies, and their effectiveness in enhancing energy efficiency while preserving temperature comfort limitations. This provides a comprehensive perspective on (1) the intricacies of achieving comfortable temperatures for occupants within structures in an energy-effective manner and (2) the relevant resources to aid scholars and professionals in addressing this challenge. Amongst the AI tools designed for energy efficiency and comfortable management, functionalities include recognizing patterns and acknowledgment, optimizing, and forecasting. This study indicates that implementing AI technologies in building management is a potential research domain. However, it remains a work in progress, as the efficacy of AI-based management is not yet fully adequate. This is primarily because of the necessity for substantial quantities of high-quality, practical information deficient in the construction and energy industry. The analysis indicates that from 2000 to 2024, the implementation of AI approaches and individualized comfort modeling has facilitated energy reductions averaging 22.5% and 45.5%, alongside comfort ness enhancements averaging between 23.5% and 86.5%. This article examines the problems encountered in utilizing AI to enhance energy usage and reassurance while outlining key future possibilities concerning AI-driven building automation for human convenience and efficient energy administration.