Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm

• Published in: Building and environment Vol. 202; p. 108026
• Main Authors: Xiong, Lei, Yao, Ye
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2021; Elsevier BV
• Subjects: Adaptability; Adaptive thermal comfort; Air conditioning; Artificial intelligence; Customization; Environmental changes; Environmental conditions; Humidity; Indoor environments; Indoor temperature; K-nearest neighbors (KNN); K-nearest neighbors algorithm; Model testing; Predicted mean vote (PMV); Predicted Mean Vote index; Temperature; Thermal analysis; Thermal comfort; Adaptive thermal comfort; K-nearest neighbors (KNN); Predicted mean vote (PMV); Indoor temperature; Humidity
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2021.108026

Compared with the static thermal comfort models like predicted mean vote (PMV) model, adaptive thermal models have a wider range of adaptability. The traditional concept of adaptive thermal comfort is that residents actively adapt to environmental changes. In this paper, a K-nearest neighbors (KNN)-based thermal comfort model is developed to establish a personalized adaptive thermal comfort environment to adapt to the preferences of the occupants. The KNN-based thermal comfort model can adjust the thermal comfort boundary for one specific individual person according to the changing environmental conditions. An artificial intelligent (AI) environmental controller has been built for studying the KNN-based thermal comfort model. 34 volunteers have been invited for testing the effectiveness of the KNN-based thermal comfort model. The test results manifested that the percent accuracy of the KNN model with 1000 sets of training data could reach up to 88.31% and can meet practical demand. The proposed thermal comfort model can help different people establish their personal indoor thermal comfort environment and promote the development of the intelligent and personalized air-conditioning systems. •A K-nearest neighbors (KNN)-based thermal comfort model is developed.•It establishes personalized adaptive thermal comfort environment.•An artificial intelligent (AI) environmental controller has been built.•It helps different people establish their personal thermal comfort environment.•It promotes the development of personalized air-conditioning systems.