[1] Meng, L. & Hossain, M. 2013. "Estimation and prediction of residential building energy consumption in rural areas of chongqing," International Journal of Engineering, vol. 26, no. 9, pp. 955-962, 2013.
[2] Hawks, M. & Cho, S. 2024. "Review and analysis of current solutions and trends for zero energy building (ZEB) thermal systems," Renewable and Sustainable Energy Reviews, vol. 189, p. 114028, 2024.
[3] Kayaçetin, N. C. & Hozatlı, B. 2024. "Whole life carbon assessment of representative building typologies for nearly zero energy building definitions," Journal of Building Engineering, vol. 95, p. 110214.
[4] AlizadehKharazi, B., Alvanchi, A. & Taghaddos, H. 2020. "A novel building information modeling-based method for improving cost and energy performance of the building envelope," International Journal of Engineering, vol. 33, no. 11, pp. 2162-2173.
[5] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. 2022. "AI in health and medicine," Nature medicine, vol. 28, no. 1, pp. 31-38.
[6] Roberts, P. 2024. Game AI Uncovered: Volume Two. CRC Press, 2024.
[7] Blagden, C. 1975. "XII. Experiments and observations in an heated room," Philosophical transactions of the Royal Society of London, no. 65, pp. 111-123.
[8] Richet, C. 2023. Thirty years of psychical research. BoD–Books on Demand.
[9] Gagge, A. P. & Gonzalez, R. R. 2010. "Mechanisms of heat exchange: biophysics and physiology," Comprehensive physiology, pp. 45-84.
[10] Givoni, B. 2010. "Thermal comfort issues and implications in high-density cities," Designing high-density cities for social and environmental sustainability, pp. 87-106.
[11] Fanger, P. 1988. "Fundamentals of thermal comfort," in Advances in Solar Energy Technology: Elsevier, pp. 3056-3061.
[12] Wu, Y., Jiang, A., Liu, H., Li, B. & Kosonen, R. 2024. "Climate chamber investigation of the effect of indoor thermal histories on thermal adaptation in different seasons," Energy and Built Environment, vol. 5, no. 3, pp. 455-463.
[13] Upadhyay, K., Elangovan, R. & Subudhi, S. 2024. "Quantifying human thermal adaptation for indoor environments through field surveys and climate chamber experiments," Advances in Building Energy Research, pp. 1-36.
[14] De Dear, R. 2007. "Adaptive comfort applications in Australia and impacts on building energy consumption," in Proceedings of the sixth international conference on indoor air quality, ventilation and energy conservation in buildings: sustainable built environment, Sendai, Japan, pp. 28-31.
[15] Jain, M., A. Singh, & Chandan, V. 2017. "Portable+ A Ubiquitous and Smart Way Towards Comfortable Energy Savings," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 2, pp. 1-22.
[16] Favero, M., Luparelli, A. & Carlucci, S. 2023. "Analysis of subjective thermal comfort data: A statistical point of view," Energy and Buildings, vol. 281, p. 112755.
[17] Khodadadi, M., Riazi, L. & Yazdani, S. 2024. "A Novel Ensemble Deep Learning Model for Building Energy Consumption Forecast," International Journal of Engineering, vol. 37, no. 6, pp. 1067-1075, 2024.
[18] Hou, Z., Lyu, J., Wu, D., Chen, J., Shi, J. & Lian, Z. 2024. "Occupant-Centric Cabin Thermal Sensation Assessment System Based on Low-Cost Thermal Imaging," Building and Environment, p. 111692.
[19] Zhang, S., Yao, R., Wei, H. & Li, B. 2024. "Prediction of occupant thermal state via infrared thermography and explainable AI," Energy and Buildings, vol. 312, p. 114153.
[20] Ren, J., Zhang, R., Cao, X. & Kong, X. 2024. "Experimental study on the physiological parameters of occupants under different temperatures and prediction of their thermal comfort using machine learning algorithms," Journal of Building Engineering, vol. 84, p. 108676.
[21] Khedr, A. M. 2024. "Enhancing supply chain management with deep learning and machine learning techniques: A review," Journal of Open Innovation: Technology, Market, and Complexity, p. 100379.
[22] Tsague, C. B. N., Seutche, J. C. N., Djeusu, L. N., Chara-Dakou, V. S. & Tchinda, R. 2024. "Application of neural networks to predict indoor air temperature in a building with artificial ventilation: impact of early stopping," International Journal of Information Technology, pp. 1-22.
[23] Nihar, K., Jain, R. K. & Cheong, S. M. 2024. "Predicting indoor personalized heat stress using wearable sensors and data-driven models," Journal of Building Engineering, vol. 97, p. 110761, 2024.
[24] Zheng, Y. & Xu, X. 2024. "Predicting indoor 3D airflow distribution using artificial neural networks with two different architectures," Energy and Buildings, vol. 303, p. 113841.
[25] Farhan, A. A., Pattipati, K., Wang, B. & Luh, P. 2015. "Predicting individual thermal comfort using machine learning algorithms," in 2015 IEEE International conference on automation science and engineering (CASE), IEEE, pp. 708-713.
[26] Bin, S. & Ke, H. 2010. "Indoor thermal comfort pmv index prediction based on particle swarm algorithm and least square support vector machine," in 2010 International Conference on Intelligent System Design and Engineering Application, vol. 1: IEEE, pp. 857-860.
[27] Chaudhuri, T., Zhai, D., Soh, Y. C., Li, H. & Xie, L. 2018. "Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology," Energy and Buildings, vol. 166, pp. 391-406.
[28] Li, D., Menassa, C. C. & Kamat, V. R. 2017. "Personalized human comfort in indoor building environments under diverse conditioning modes, "Building and Environment, vol. 126, pp. 304-317.
[29] Auffenberg, F., Stein, S., & Rogers, A. 2015. "A personalised thermal comfort model using a Bayesian network".
[30] Ghahramani, A., Castro, G., Karvigh, S. A., & Becerik-Gerber, B. 2018. "Towards unsupervised learning of thermal comfort using infrared thermography," Applied Energy, vol. 211, pp. 41-49.
[31] Ličina, V. F. et al., 2018. "Development of the ASHRAE global thermal comfort database II," Building and Environment, vol. 142, pp. 502-512.
