Data Challenges in AI-Driven HVAC Systems: A Critical Analysis and Future Directions

Abstract Integrating Artificial Intelligence (AI) into heating, ventilation, and air conditioning (HVAC) systems is a promising approach that helps enhance energy efficiency in buildings, which leads to cost savings and provides environmental benefits. However, the effective performance of the AI models depends not only on the model design but also on the data quality, reliability, size, availability, and management. This paper analyses recent studies that apply AI models, specifically Deep Learning and Hybrid models, to achieve energy efficiency in HVAC systems in buildings from a data perspective, examining various aspects of data management. This analysis aims to provide insights into data-related challenges in AIdriven HVAC systems and propose strategies to overcome them, ensuring more accurate, efficient, and reliable models. The findings reveal that combining multiple data types can enhance model performance and generalizability. The findings also indicate that data quality is overlooked by researchers in many studies, where only 31 % of the analysed papers discussed quality issues, reflecting that it is not yet a standard practice in this field. Additionally, this analysis highlights the scarcity of reliable and audited data. Therefore, and in response to this issue, this paper recommends accessible and reliable data resources that can be employed in AI applications for HVAC systems in buildings.