| Abstract | 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. |
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