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Seasonal forecasts of wind power generation

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  • Lledó, Ll.
  • Torralba, V.
  • Soret, A.
  • Ramon, J.
  • Doblas-Reyes, F.J.

Abstract

The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe.

Suggested Citation

  • Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:91-100
    DOI: 10.1016/j.renene.2019.04.135
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    References listed on IDEAS

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    Cited by:

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    5. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    6. Bastien Alonzo & Philippe Drobinski & Riwal Plougonven & Peter Tankov, 2020. "Measuring the Risk of Supply and Demand Imbalance at the Monthly to Seasonal Scale in France," Energies, MDPI, vol. 13(18), pages 1-21, September.
    7. Katopodis, Theodoros & Markantonis, Iason & Vlachogiannis, Diamando & Politi, Nadia & Sfetsos, Athanasios, 2021. "Assessing climate change impacts on wind characteristics in Greece through high resolution regional climate modelling," Renewable Energy, Elsevier, vol. 179(C), pages 427-444.
    8. Lledó, Llorenç & Ramon, Jaume & Soret, Albert & Doblas-Reyes, Francisco-Javier, 2022. "Seasonal prediction of renewable energy generation in Europe based on four teleconnection indices," Renewable Energy, Elsevier, vol. 186(C), pages 420-430.
    9. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    10. Hai Lin & Yi Yang & Shuguang Wang & Shuyu Wang & Jianping Tang & Guangtao Dong, 2023. "Evaluation of MSWX Bias-Corrected Meteorological Forcing Datasets in China," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
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    12. Olaofe, Z.O., 2019. "Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based)," Energy, Elsevier, vol. 189(C).

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