On the use of robust regression methods in wind speed assessment
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DOI: 10.1016/j.renene.2016.08.009
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Cited by:
- Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
- Commin, Andrew N. & French, Andrew S. & Marasco, Matteo & Loxton, Jennifer & Gibb, Stuart W. & McClatchey, John, 2017. "The influence of the North Atlantic Oscillation on diverse renewable generation in Scotland," Applied Energy, Elsevier, vol. 205(C), pages 855-867.
- He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
- Zhang, Chu & Li, Zhengbo & Ge, Yida & Liu, Qianlong & Suo, Leiming & Song, Shihao & Peng, Tian, 2024. "Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD," Energy, Elsevier, vol. 296(C).
- Takvor H. Soukissian & Dimitra Denaxa & Flora Karathanasi & Aristides Prospathopoulos & Konstantinos Sarantakos & Athanasia Iona & Konstantinos Georgantas & Spyridon Mavrakos, 2017. "Marine Renewable Energy in the Mediterranean Sea: Status and Perspectives," Energies, MDPI, vol. 10(10), pages 1-56, September.
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Keywords
Offshore wind energy; Robust methods; Calibration; Outliers; Mediterranean sea;All these keywords.
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