Analysis of User Thermal Preference Prediction Model and Control Method Based on Air Conditioning Usage Behavior Records
This paper expounds that predicting individual thermal preferences of air conditioning users can contribute to the development of intelligent air conditioning control technology.It analyzes the embedded sensor data in the air conditioner and the interaction behavior between users and these devices,and uses machine learning algorithms to predict the set temperature changes of the air conditioner.The results indicate that there are significant differences in temperature preferences and adjustment time among different users in their air conditioning usage behavior.More than 60％of users tend to set the air conditioning temperature within the range of 25-28℃.The accuracy of machine learning models in predicting the increase and decrease of set temperatures is 72.1％～87.3％.By adding months and hours as input features,the performance of the model improves with the increase of samples.
data-driven modelssmart homesbehavioral analysisair conditioning control
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