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On the use of robust regression methods in wind speed assessment

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  • Soukissian, Takvor H.
  • Karathanasi, Flora E.

Abstract

Wind climate analysis and modelling is of most importance during site selection for offshore wind farm development. In this regard, reliable long-term wind data are required. Buoy measurements are considered as a reference source in relevant applications including evaluation and calibration of wind data obtained from less reliable sources, combined assessment, blending and homogenization of multi-source wind data, etc. Most of these applications are based on regression techniques elaborated by using the principle of ordinary least squares (OLS). However, wind data usually contain several outliers, which may question the validity of the regression analysis, if not properly considered. This study is focused on the implementation of the most important robust regression methods, which can identify and reveal outliers, and retain at the same time their efficiency. Long-term reference wind data series obtained from buoys at six locations in the Mediterranean Sea are used to calibrate hindcast (model) wind data by applying robust methods and OLS. The obtained results are compared according to several statistical measures. The effects of the calibration methods are also assessed with respect to the available wind power potential. The results clearly suggest that least trimmed squares and L1-estimator perform in all respects better than OLS.

Suggested Citation

  • Soukissian, Takvor H. & Karathanasi, Flora E., 2016. "On the use of robust regression methods in wind speed assessment," Renewable Energy, Elsevier, vol. 99(C), pages 1287-1298.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:1287-1298
    DOI: 10.1016/j.renene.2016.08.009
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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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