Study on data augmentation with physics-informed generative adversarial networks and the extrapolation performance of COP prediction for chillers

•PI-WGAN framework improves COP prediction under sparse data and distribution shift.•Vendi Score and Mahalanobis distance quantify data diversity and extrapolation risk.•Physics-informed samples enhance physical consistency and statistical reliability.•MAPE reduced from 41.2 % to 136.3 % to 4.97 %–1...

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Vydané v:	Energy conversion and management Ročník 346; s. 120418
Hlavní autori:	Wang, Zhengyang, Chen, Jun, Guo, Kexin, Xu, Bo, Chen, Zhenqian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.12.2025
Predmet:	COP prediction Data augmentation Generative adversarial network Physics-informed neural network Uncertainty quantification COP prediction Uncertainty quantification Data augmentation Physics-informed neural network Generative adversarial network
ISSN:	0196-8904
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