Predicting education building occupants’ thermal sensation through CatBoost-DF algorithm

ABSTRACT

A novel machine learning method, named CatBoost-DF (CatBoost deep forest), is proposed to solve this existing problem of low accuracy and lack of practicality in thermal sensation prediction. In the CatBoost-DF, a cascading strategy is introduced to strengthen the association between each layer of CatBoost. To verify the accuracy and robustness of CatBoost-DF, experiments collected physiological and environmental data from hundreds of subjects with the help of sensor devices and questionnaires. Compared with existing state-of-the-art machine learning methods, CatBoost-DF shows significant superiority, with a prediction accuracy of 90%, which is 4%-39% higher than other models. Moreover, the study explored the effects of seasonal and gender factors on thermal sensation. Result shown that different seasons have different thermal sensation for males and females. Finally, CatBoost-DF is applied to predict occupants’ thermal sensation, and the “comfort range” of the important parameters HR, WS, and CTR that affect the thermal sensation is calculated experimentally.

Acknowledgements

The research work was supported by the Science and Technology Plan Project of Henan province under Grant No. 222102210182, Doctoral Foundation of Henan Polytechnic University under Grant No. B2021-31, Scientific Studies of Higher Education Institution of Henan province under Grant 22A520029, Natural Science Foundation of Henan Province under Grant No. 222300420168, and the Fundamental Research Funds for the Universities of Henan Province under Grant NSFRF220415.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

The work was supported by the Hainan Provincial Natural Science Foundation of China [222300420168]; Doctoral Foundation of Henan Polytechnic University [B2021-31]; the Fundamental Research Funds for the Universities of Henan Province [NSFRF220415]; Scientific Studies of Higher Education Institution of Henan province [22A520029]; Science and Technology Plan Project of Henan province [222102210182].