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Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Networks

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Cited by:

  1. Ding, Jun-Wei & Chuang, Ming-Ju & Tseng, Jing-Siou & Hsieh, I-Yun Lisa, 2024. "Reanalysis and Ground Station data: Advanced data preprocessing in deep learning for wind power prediction," Applied Energy, Elsevier, vol. 375(C).
  2. Rogers, T.J. & Gardner, P. & Dervilis, N. & Worden, K. & Maguire, A.E. & Papatheou, E. & Cross, E.J., 2020. "Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression," Renewable Energy, Elsevier, vol. 148(C), pages 1124-1136.
  3. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
  4. Wang, Yibo & Shao, Xinyao & Liu, Chuang & Cai, Guowei & Kou, Lei & Wu, Zhiqiang, 2019. "Analysis of wind farm output characteristics based on descriptive statistical analysis and envelope domain," Energy, Elsevier, vol. 170(C), pages 580-591.
  5. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).
  6. 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.
  7. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
  8. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
  9. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Kıyan, Emel, 2022. "Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study," Energy, Elsevier, vol. 239(PB).
  10. Zou, Runmin & Yang, Jiaxin & Wang, Yun & Liu, Fang & Essaaidi, Mohamed & Srinivasan, Dipti, 2021. "Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer," Applied Energy, Elsevier, vol. 304(C).
  11. Li, Xuan & Zhang, Wei, 2020. "Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions," Renewable Energy, Elsevier, vol. 159(C), pages 570-584.
  12. 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).
  13. Kebir, Anouer & Woodward, Lyne & Akhrif, Ouassima, 2019. "Real-time optimization of renewable energy sources power using neural network-based anticipative extremum-seeking control," Renewable Energy, Elsevier, vol. 134(C), pages 914-926.
  14. Mushtaq, Khurram & Waris, Asim & Zou, Runmin & Shafique, Uzma & Khan, Niaz B. & Khan, M. Ijaz & Jameel, Mohammed & Khan, Muhammad Imran, 2024. "A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy," Energy, Elsevier, vol. 304(C).
  15. Sebastiani, Alessandro & Angelou, Nikolas & Peña, Alfredo, 2024. "Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar," Applied Energy, Elsevier, vol. 364(C).
  16. Moss, Coleman & Maulik, Romit & Iungo, Giacomo Valerio, 2024. "Augmenting insights from wind turbine data through data-driven approaches," Applied Energy, Elsevier, vol. 376(PA).
  17. Sebastiani, Alessandro & Peña, Alfredo & Troldborg, Niels, 2023. "Numerical evaluation of multivariate power curves for wind turbines in wakes using nacelle lidars," Renewable Energy, Elsevier, vol. 202(C), pages 419-431.
  18. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
  19. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
  20. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
  21. Khurram Mushtaq & Runmin Zou & Asim Waris & Kaifeng Yang & Ji Wang & Javaid Iqbal & Mohammed Jameel, 2023. "Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-25, August.
  22. Karamichailidou, Despina & Kaloutsa, Vasiliki & Alexandridis, Alex, 2021. "Wind turbine power curve modeling using radial basis function neural networks and tabu search," Renewable Energy, Elsevier, vol. 163(C), pages 2137-2152.
  23. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
  24. Díaz, Santiago & Carta, José A. & Castañeda, Alberto, 2020. "Influence of the variation of meteorological and operational parameters on estimation of the power output of a wind farm with active power control," Renewable Energy, Elsevier, vol. 159(C), pages 812-826.
  25. Qiao, Yanhui & Han, Shuang & Zhang, Yajie & Liu, Yongqian & Yan, Jie, 2024. "A multivariable wind turbine power curve modeling method considering segment control differences and short-time self-dependence," Renewable Energy, Elsevier, vol. 222(C).
  26. Hu, Yang & Xi, Yunhua & Pan, Chenyang & Li, Gengda & Chen, Baowei, 2020. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating," Renewable Energy, Elsevier, vol. 146(C), pages 2095-2111.
  27. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
  28. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.
  29. Lio, Wai Hou & Li, Ang & Meng, Fanzhong, 2021. "Real-time rotor effective wind speed estimation using Gaussian process regression and Kalman filtering," Renewable Energy, Elsevier, vol. 169(C), pages 670-686.
  30. Liang, Guoyuan & Su, Yahao & Wu, Xinyu & Ma, Jiajun & Long, Huan & Song, Zhe, 2023. "Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty," Renewable Energy, Elsevier, vol. 216(C).
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