Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy
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DOI: 10.1016/j.renene.2019.03.047
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- Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
- Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
- Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
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- Hu, Shuai & Xiang, Yue & Zhang, Hongcai & Xie, Shanyi & Li, Jianhua & Gu, Chenghong & Sun, Wei & Liu, Junyong, 2021. "Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction," Applied Energy, Elsevier, vol. 293(C).
- Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
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Keywords
Condition monitoring; Power curve; Covariance functions; Gaussian process;All these keywords.
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