An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms
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DOI: 10.1016/j.energy.2023.128518
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- Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
- Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Ahmad Hassan Kamal & Muhammad Haider Khan & Awais Ahmad Khan & Hassan Afroze Ahmad & Fahad Ahmed & Noman Hafeez & Rana Muhammad Zawar Sami & Syed Muhammad , 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation," Energies, MDPI, vol. 13(21), pages 1-22, October.
- Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
- Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
- Chen, Xiaoyuan & Zhang, Mingshun & Jiang, Shan & Gou, Huayu & Zhou, Pang & Yang, Ruohuan & Shen, Boyang, 2023. "Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage," Energy, Elsevier, vol. 263(PA).
- Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
- Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
- Wang, Yun & Duan, Xiaocong & Zou, Runmin & Zhang, Fan & Li, Yifen & Hu, Qinghua, 2023. "A novel data-driven deep learning approach for wind turbine power curve modeling," Energy, Elsevier, vol. 270(C).
- Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Li, Wenqiang & Peng, Pei, 2021. "A hybrid deep transfer learning strategy for short term cross-building energy prediction," Energy, Elsevier, vol. 215(PB).
- Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
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Keywords
Wind Turbines (WTs); Federated transfer learning; Lightweight model; Partial aggregation; Collaborative monitoring;All these keywords.
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