Algorithms for Deriving Occupant Preference Ranges in Automatic Control of Air Conditioners

초록

The automatic control system of air conditioners has recently gained attention as an effective approach to reducing energy consumption in buildings. The primary goal of these systems is to achieve both energy efficiency and thermal comfort for occupants. However, if the system fails to ensure occupant satisfaction even with energy-efficient operation, it risks being underutilized and losing its intended effectiveness. Therefore, for successful implementation, these systems must minimize user intervention by providing thermal comfort tailored to individual preferences. Conventional systems predominantly rely on PMV-PPD-based thermal comfort models, but these models often face limitations in real-world applications due to restricted parameters available for data collection and the discrepancy between individual thermal preferences and PMV- based statistical predictions. To address these challenges, this study proposes a method to derive personalized comfort ranges using data that can be practically collected on-site, including air conditioner control histories. Through experiments, user control patterns in specific thermal environments (temperature and humidity) were collected and utilized to develop both rule-based and machine learning-based models. To validate the models, occupant thermal preferences were surveyed and compared with the comfort ranges predicted by the models to assess their reliability. This study presents a data-driven approach for developing occupant-specific thermal comfort models, contributing to the practical implementation and advancement of autonomous air conditioner control systems.

키워드