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Trend Lines and Japanese Candlesticks Applied to the Forecasting of Wind Speed Data Series

Author

Listed:
  • Manfredo Guilizzoni

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Paloma Maldonado Eizaguirre

    (Minsait, Avenida de Bruselas 35, 28108 Alcobendas, Spain)

Abstract

One of the most critical issues for wind energy exploitation is the high variability of the resource, resulting in very difficult forecasting of the power that wind farms can grant. A vast literature has therefore been devoted to wind speed and wind power quantitative forecasting, using different techniques. The widely used statistical and learning models that are based on a continuation in the future of the series’ past behaviour offer a performance that may be much less satisfactory when the values suddenly change their trend. The application to wind speed data of two techniques usually employed for the technical analysis of financial series–namely support and resistances identification and candlestick charts–is investigated here, with the main aim to detect inversion points in the series. They are applied to wind speed data series for two locations in Spain and Italy. The proposed indicators confirm their usefulness in identifying peculiar behaviours in the system and conditions where the trend may be expected to change. This additional information offered to the forecasting algorithms may also be included in innovative approaches, e.g., based on machine learning.

Suggested Citation

  • Manfredo Guilizzoni & Paloma Maldonado Eizaguirre, 2022. "Trend Lines and Japanese Candlesticks Applied to the Forecasting of Wind Speed Data Series," Forecasting, MDPI, vol. 4(1), pages 1-17, January.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:1:p:9-181:d:735681
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    References listed on IDEAS

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    1. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    2. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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    2. Irina Meghea, 2023. "Comparison of Statistical Production Models for a Solar and a Wind Power Plant," Mathematics, MDPI, vol. 11(5), pages 1-16, February.

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