An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive vision-based personal comfort model that integrates thermographic images and deep learning. Unlike previous studies, the entire thermographic image of the upper body is directly used during model training, minimizing complex data processing and maximizing the use of rich skin temperature distribution. The proposed method is validated using thermographic images and corresponding thermal sensation votes (TSV) from 10 participants under different experimental conditions. Results show that the model based on a 3-point TSV scale achieves exceptional classification performance with an average accuracy of 99.51 %, outperforming existing models. The model performance using a 7-point TSV scale is slightly lower, with an average accuracy of 89.90 %. This method offers potential for integrating thermal comfort models into real-time building environmental control, optimizing occupant comfort and energy consumption.

中文翻译：

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使用热成像图像和深度学习的基于视觉的非侵入性个人舒适度模型


预测居住者热舒适度的有效方法对于制定最佳环境控制策略同时最大限度地减少建筑物的能源消耗至关重要。本文提出了一种基于视觉的无创个人舒适模型，该模型集成了热成像图像和深度学习。与以前的研究不同，上半身的整个热成像图像在模型训练期间直接使用，最大限度地减少了复杂的数据处理，并最大限度地利用了丰富的皮肤温度分布。在不同实验条件下，使用来自 10 名参与者的热成像图像和相应的热感觉投票 （TSV） 验证了所提出的方法。结果表明，基于 3 点 TSV 量表的模型实现了卓越的分类性能，平均准确率为 99.51%，优于现有模型。使用 7 点 TSV 量表的模型性能略低，平均准确率为 89.90 %。这种方法为将热舒适模型集成到实时建筑环境控制中提供了潜力，从而优化了居住者的舒适度和能源消耗。