A Random Forest Classification Algorithm Based Personal Thermal Sensation Model for Personalized Conditioning System in Office Buildings

The personal thermal sensation model is used as the main component for personalized conditioning system, which is an effective method to fulfill thermal comfort requirements of the occupants, considering the energy consumption. The Random Forest classification algorithm based thermal sensation model is developed in this study, which combines indoor air quality parameters, personal information, physiological factors and occupancy preferences on selection of 7-level of sensation: cold, cool, slightly cool, neutral, slightly warm, warm and hot. Our model shows better functionality, as well as performance and factor selection. As a result, our method has achieved 70.2% accuracy, comparing with the 57.4% accuracy of support vector machine, and 67.7% accuracy of neutral network in an ASHRAE RP-884 database. Therefore, our newly developed model can be used in personalized thermal adjustment systems with intelligent control functions.