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.

与预测平均投票（PMV）模型等静态热舒适模型相比，自适应热模型具有更广泛的适应性。适应性热舒适的传统概念是居民主动适应环境变化。在本文中，开发了基于 K-最近邻 (KNN) 的热舒适模型，以建立个性化的自适应热舒适环境，以适应居住者的喜好。基于 KNN 的热舒适模型可以根据不断变化的环境条件调整特定个体的热舒适边界。已经构建了一个人工智能 (AI) 环境控制器来研究基于 KNN 的热舒适模型。已邀请 34 名志愿者测试基于 KNN 的热舒适模型的有效性。测试结果表明，1000组训练数据的KNN模型的准确率可达88.31%，可以满足实际需求。所提出的热舒适模型可以帮助不同的人建立自己的室内热舒适环境，促进智能、个性化空调系统的发展。