Explainable machine learning models to predict outlet water temperature of pipe-type energy pile
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DOI: 10.1016/j.renene.2025.122972
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- Guo, Yachen & Wang, Chenglong & Bouazza, Abdelmalek & Kong, Gangqiang & Ding, Xuanming, 2024. "An approach for heat transfer thermal analysis of a pre-stressed high-strength concrete (PHC) energy pile," Renewable Energy, Elsevier, vol. 235(C).
- Pei, Huafu & Song, Huaibo & Meng, Fanhua & Liu, Weiling, 2022. "Long-term thermomechanical displacement prediction of energy piles using machine learning techniques," Renewable Energy, Elsevier, vol. 195(C), pages 620-636.
- Cunha, R.P. & Bourne-Webb, P.J., 2022. "A critical review on the current knowledge of geothermal energy piles to sustainably climatize buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
- Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
- Wang, Chenglong & Zhu, Pengxi & Bouazza, Abdelmalek & Kong, Gangqiang & Ding, Xuanming, 2024. "An approach for thermal performance assessment and optimization design of energy diaphragm walls (EDWs) with seasonal thermal load via numerical modeling," Renewable Energy, Elsevier, vol. 237(PD).
- Zhang, Weiyi & Zhou, Haiyang & Bao, Xiaohua & Cui, Hongzhi, 2023. "Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN–LSTM hybrid model," Energy, Elsevier, vol. 264(C).
- Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
- Faizal, Mohammed & Bouazza, Abdelmalek & McCartney, John S., 2022. "Thermal resistance analysis of an energy pile and adjacent soil using radial temperature gradients," Renewable Energy, Elsevier, vol. 190(C), pages 1066-1077.
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