Exploring the comprehensive integration of artificial intelligence in optimizing HVAC system operations: A review and future outlook - Shengze Lu; Shiyu Zhou; Yan Ding; Moon Keun Kim; Bin Yang; Zhe Tian; Jiying Liu (Results in Engineering)
With the rapid development of the artificial intelligence (AI) technology, its application in optimizing heating, ventilation and air-conditioning (HVAC) systems operation is becoming increasingly widespread. This study reviews the latest advances in AI optimization for HVAC systems operation, systematically outlining the characteristics of the AI technology and its various application methods in air conditioning systems. The main features of the AI technology are first introduced. The main algorithms of supervised learning, reinforcement learning, and deep learning are then analyzed in the fields of air conditioning operation optimization, energy consumption prediction and control, indoor environmental protection, and fault detection and diagnosis. The combination of the AI and digital twin technologies is also explored. This review study focuses on the intelligent control technology, multi-objective optimization of system operation, system optimization based on occupant behavior and thermal comfort, and system fault detection and diagnosis. Although the AI technology has led to satisfactory results in air conditioning system optimization, its practical applications still face challenges, such as the model accuracy and generalization ability, applicability, and integration with existing systems. The analysis conducted in this paper provides a foundation for the optimization of HVAC system operation.