Intrusive and non-intrusive early warning systems for thermal discomfort by analysis of body surface temperature
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DOI: 10.1016/j.apenergy.2022.120283
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References listed on IDEAS
- Uğursal, Ahmet & Culp, Charles H., 2013. "The effect of temperature, metabolic rate and dynamic localized airflow on thermal comfort," Applied Energy, Elsevier, vol. 111(C), pages 64-73.
- Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
- Ghahramani, Ali & Castro, Guillermo & Karvigh, Simin Ahmadi & Becerik-Gerber, Burcin, 2018. "Towards unsupervised learning of thermal comfort using infrared thermography," Applied Energy, Elsevier, vol. 211(C), pages 41-49.
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- Yin, Linfei & Zhou, Hang, 2024. "Modal decomposition integrated model for ultra-supercritical coal-fired power plant reheater tube temperature multi-step prediction," Energy, Elsevier, vol. 292(C).
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Keywords
Thermal comfort; Energy conservation; Relative thermal sensation; Physiological index; Infrared thermography; Machine learning;All these keywords.
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