Thermal comfort is a condition of mind that expresses satisfaction with the thermal environment. Thermal comfort is critical for both health and productivity. Inadequate thermal comfort results in stress for building inhabitants. Improved thermal conditions are directly related to improved health and productivity of individuals. This paper proposes a novel human thermal comfort model using machine learning algorithms that identify the key features and predict thermal sensation with higher accuracy. We evaluate our approach using tenfold cross-validation and compare our results with state-of-the-art Fanger’s model. Our approach achieves a higher accuracy of 86.08%. Our results demonstrate the potential of our approach to predict thermal sensation votes under wide-ranging thermal conditions correctly.

热舒适是一种表示对热环境满意的心理状态。热舒适度对健康和生产力都至关重要。热舒适度不足会给建筑居民带来压力。改善的热条件与改善个人的健康和生产力直接相关。本文提出了一种使用机器学习算法的新型人体热舒适模型，该模型可识别关键特征并更准确地预测热感觉。我们使用十倍交叉验证评估我们的方法，并将我们的结果与最先进的 Fanger 模型进行比较。我们的方法达到了 86.08% 的更高准确率。我们的结果证明了我们的方法在广泛的热条件下正确预测热感觉投票的潜力。