Non-invasive vision-based personal comfort model using thermographic images and deep learning

• Published in: Automation in construction 2024-12, Vol.168, p.105811, Article 105811
• Main Authors: Zakka, Vincent Gbouna, Lee, Minhyun, Zhang, Ruixiaoxiao, Huang, Lijie, Jung, Seunghoon, Hong, Taehoon
• Format: Article
• Language: English
• Subjects: Deep learning; Non-invasive approach; Occupant thermal comfort; Occupant-centric control; Personal comfort model; Thermographic imaging
• ISSN: 0926-5805
• DOI: 10.1016/j.autcon.2024.105811

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. •Thermographic image and deep learning are used to develop a personal comfort model.•Data was collected from 10 participants under varying temperature conditions.•Two personal comfort models based on both 3- and 7-point TSV scales are developed.•The proposed models show high accuracy (78–100 %), outperforming existing models.•Using a full thermographic image of upper body can better capture thermal response.