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Short-term wind speed forecasting by spectral analysis from long-term observations with missing values

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  • Akçay, Hüseyin
  • Filik, Tansu

Abstract

In this paper, we propose a novel wind speed forecasting framework. The performance of the proposed framework is assessed on the wind speed measurements collected from the five meteorological stations in the Marmara region of Turkey. The experimental results show that trimming of the diurnal, the weekly, the monthly, and the annual patterns in the measurements significantly enhances the estimation accuracy. The proposed framework builds on data de-trending, covariance-factorization via a recently developed subspace method, and one-step-ahead and/or multi-step-ahead Kalman filter prediction ideas. The data sets do not have to be complete. In fact, as in sensor failures, intermittently or sequentially missing measurements are permitted. The numerical experiments on the real data sets show that the wind speed forecasts, in particular the multi-step-ahead forecasts, outperform the benchmark values computed with the persistence forecasting models by a clear difference.

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  • Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    • Handle: RePEc:eee:appene:v:191:y:2017:i:c:p:653-662
    DOI: 10.1016/j.apenergy.2017.01.063
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    12. Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang & Hsin-Jen Chen, 2023. "One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods," Energies, MDPI, vol. 16(6), pages 1-22, March.
    13. Esra’a Alshdaifat & Doa’a Alshdaifat & Ayoub Alsarhan & Fairouz Hussein & Subhieh Moh’d Faraj S. El-Salhi, 2021. "The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance," Data, MDPI, vol. 6(2), pages 1-23, January.
    14. Qin, Li & Liu, Shi & Kang, Yi & Yan, Song An & Inaki Schlaberg, H. & Wang, Zhan, 2019. "Wind velocity distribution reconstruction using CFD database with Tucker decomposition and sensor measurement," Energy, Elsevier, vol. 167(C), pages 1236-1250.
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