Toward smarter HVAC control: Machine learning reveals hidden drivers in thermal comfort databases

Interpretable thermal-comfort modeling requires more than accuracy: it requires clarity about which inputs matter and how missing data policies reshape those conclusions. We present a quality-aware sensitivity framework that couples cross-dataset quality labeling with perturbation analysis to quanti...

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Podrobná bibliografie

Hlavní autoři:	Hongshan Guo, Ilaria Pigliautile, Yu Chang, Qingyao Qiao, Yichun Li
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2026-05-01
Edice:	Energy and AI
Témata:	Thermal sensation Thermal Comfort Database Sensitivity analysis Predictive thermal comfort AutoML
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S2666546826000352
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