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Sub-hourly forecasting of wind speed and wind energy

Author

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  • Dupré, Aurore
  • Drobinski, Philippe
  • Alonzo, Bastien
  • Badosa, Jordi
  • Briard, Christian
  • Plougonven, Riwal

Abstract

The need to have access to accurate short term forecasts is essential in order to anticipate the energy production from intermittent renewable sources, notably wind energy. For hourly and sub-hourly forecasts, benchmarks are based on statistical approaches such as time series based methods or neural networks, which are always tested against persistence. Here we discuss the performances of downscaling approaches using information from Numerical Weather Prediction (NWP) models, rarely used at those time scales, and compare them with the statistical approaches for the wind speed forecasting at hub height. The aim is to determine the added value of Model Output Statistics for sub-hourly forecasts of wind speed, compared to the classical time series based methods. Two downscaling approaches are tested: one using explanatory variables from NWP model outputs only and another which additionally includes local wind speed measurements. Results of both approaches and of the classical time series based methods, tested against persistence on a specific wind farm, are considered. For both hourly and sub-hourly forecasts, adding explanatory variables derived from observations in the downscaling models gives higher improvements over persistence than the benchmark methods and than the downscaling models using only the NWP model outputs.

Suggested Citation

  • Dupré, Aurore & Drobinski, Philippe & Alonzo, Bastien & Badosa, Jordi & Briard, Christian & Plougonven, Riwal, 2020. "Sub-hourly forecasting of wind speed and wind energy," Renewable Energy, Elsevier, vol. 145(C), pages 2373-2379.
  • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:2373-2379
    DOI: 10.1016/j.renene.2019.07.161
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    Citations

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    Cited by:

    1. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Aurore Dupré & Philippe Drobinski & Jordi Badosa & Christian Briard & Peter Tankov, 2020. "The Economic Value of Wind Energy Nowcasting," Energies, MDPI, vol. 13(20), pages 1-20, October.
    3. Liu, Hui & Yang, Rui & Wang, Tiantian & Zhang, Lei, 2021. "A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections," Renewable Energy, Elsevier, vol. 165(P1), pages 573-594.
    4. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).
    5. Jafarzadeh Ghoushchi, Saeid & Manjili, Sobhan & Mardani, Abbas & Saraji, Mahyar Kamali, 2021. "An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: A case study in wind power plant," Energy, Elsevier, vol. 223(C).
    6. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    7. Singh, Sarvesh Kumar & Lohani, Bharat & Arora, Lavish & Choudhary, Devendra & Nagarajan, Balasubramanian, 2020. "A visual-inertial system to determine accurate solar insolation and optimal PV panel orientation at a point and over an area," Renewable Energy, Elsevier, vol. 154(C), pages 223-238.
    8. Aly, Hamed H.H., 2020. "A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting," Energy, Elsevier, vol. 213(C).
    9. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    10. Daniel Vassallo & Raghavendra Krishnamurthy & Thomas Sherman & Harindra J. S. Fernando, 2020. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting," Energies, MDPI, vol. 13(20), pages 1-19, October.
    11. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
    12. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    13. Liu, Hui & Duan, Zhu, 2020. "A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection," Applied Energy, Elsevier, vol. 261(C).
    14. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    15. Li, Jingrui & Wang, Jianzhou & Zhang, Haipeng & Li, Zhiwu, 2022. "An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China," Renewable Energy, Elsevier, vol. 201(P1), pages 766-779.
    16. Bouche, Dimitri & Flamary, Rémi & d’Alché-Buc, Florence & Plougonven, Riwal & Clausel, Marianne & Badosa, Jordi & Drobinski, Philippe, 2023. "Wind power predictions from nowcasts to 4-hour forecasts: A learning approach with variable selection," Renewable Energy, Elsevier, vol. 211(C), pages 938-947.
    17. Ogliari, Emanuele & Sakwa, Maciej & Cusa, Paolo, 2024. "Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters," Renewable Energy, Elsevier, vol. 221(C).

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