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Wind forecasting using Principal Component Analysis

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  • Skittides, Christina
  • Früh, Wolf-Gerrit

Abstract

We present a new statistical wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events. At the same time the method provides a prediction of the likely forecasting error. The method is applied to Meteorological Office wind speed and direction data from a site in Edinburgh. For the training period, the years 2008–2009 were used, and the wind forecasting was tested for the data from 2010 for that site. Different parameter values were also used in the PCA analysis to explore the sensitivity analysis of the results.

Suggested Citation

  • Skittides, Christina & Früh, Wolf-Gerrit, 2014. "Wind forecasting using Principal Component Analysis," Renewable Energy, Elsevier, vol. 69(C), pages 365-374.
    • Handle: RePEc:eee:renene:v:69:y:2014:i:c:p:365-374
    DOI: 10.1016/j.renene.2014.03.068
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    Citations

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    Cited by:

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    6. Fabrizio De Caro & Alfredo Vaccaro & Domenico Villacci, 2017. "Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data," Energies, MDPI, vol. 10(2), pages 1-14, February.
    7. Hao, Ying & Dong, Lei & Liao, Xiaozhong & Liang, Jun & Wang, Lijie & Wang, Bo, 2019. "A novel clustering algorithm based on mathematical morphology for wind power generation prediction," Renewable Energy, Elsevier, vol. 136(C), pages 572-585.
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    9. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Li, Zhuo, 2021. "Feature extraction of meteorological factors for wind power prediction based on variable weight combined method," Renewable Energy, Elsevier, vol. 179(C), pages 1925-1939.
    10. Philippe de Bekker & Sho Cremers & Sonam Norbu & David Flynn & Valentin Robu, 2023. "Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm," Energies, MDPI, vol. 16(5), pages 1-26, March.
    11. Zhang, Yagang & Yang, Jingyun & Wang, Kangcheng & Wang, Zengping & Wang, Yinding, 2015. "Improved wind prediction based on the Lorenz system," Renewable Energy, Elsevier, vol. 81(C), pages 219-226.
    12. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Fan, Xiaochao, 2016. "A new wind power prediction method based on chaotic theory and Bernstein Neural Network," Energy, Elsevier, vol. 117(P1), pages 259-271.
    13. Juan. J. Flores & José R. Cedeño González & Héctor Rodríguez & Mario Graff & Rodrigo Lopez-Farias & Felix Calderon, 2019. "Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison," Energies, MDPI, vol. 12(18), pages 1-19, September.
    14. Wang, Yifei & Ma, Xiandong & Joyce, Malcolm J., 2016. "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis," Renewable Energy, Elsevier, vol. 97(C), pages 444-456.
    15. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
    16. Wang, Jianzhou & Heng, Jiani & Xiao, Liye & Wang, Chen, 2017. "Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting," Energy, Elsevier, vol. 125(C), pages 591-613.
    17. Liu, Da & Wang, Jilong & Wang, Hui, 2015. "Short-term wind speed forecasting based on spectral clustering and optimised echo state networks," Renewable Energy, Elsevier, vol. 78(C), pages 599-608.

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