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Skillful Seasonal Prediction of Global Onshore Wind Resources in SIDRI-ESS V1.0

Author

Listed:
  • Zixiang Yan

    (Shanghai Investigation, Design and Research Institute Co., Ltd., Shanghai 200434, China
    Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate of the MOE, Department of Atmospheric and Oceanic Science and Institute of Atmospheric Science, Fudan University, Shanghai 200438, China)

  • Wen Zhou

    (Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate of the MOE, Department of Atmospheric and Oceanic Science and Institute of Atmospheric Science, Fudan University, Shanghai 200438, China
    Key Laboratory for Polar Science of the MNR, Polar Research Institute of China, Shanghai 200136, China)

  • Jinxiao Li

    (Shanghai Investigation, Design and Research Institute Co., Ltd., Shanghai 200434, China)

  • Xuedan Zhu

    (Shanghai Investigation, Design and Research Institute Co., Ltd., Shanghai 200434, China)

  • Yuxin Zang

    (Shanghai Investigation, Design and Research Institute Co., Ltd., Shanghai 200434, China)

  • Liuyi Zhang

    (Shanghai Investigation, Design and Research Institute Co., Ltd., Shanghai 200434, China)

Abstract

The seasonal variation in wind resources has a great impact on wind energy generation, affecting the maintenance planning, operational strategies, and economic benefits of wind farms. Therefore, effective seasonal prediction of wind resources is crucial for the wind power industry. This study evaluates the seasonal prediction skill for global onshore wind resources using the SIDRI-ESS V1.0 dynamic prediction system. High prediction skill for 10 m wind speed (ws10m) is observed mainly in six regions: southern North America, northern South America, western and eastern Europe, and South and East Asia. These regions already have a substantial wind power industry or possess rich wind resources and will need wind power industry deployment in the future. Prediction skill is the highest at a 1-month lead time for most regions and decays as the lead time increases. The highest skill emerges in East Asia, with a temporal correlation coefficient (TCC) reaching 0.7, and persists with a 1-month to 5-month lead time. However, the highest skill for southern North America is at a 6-month lead time. Additionally, ensemble prediction effectively reduces uncertainty, such that a multi-member ensemble mean always matches or even exceeds the individual ensemble member with the best performance. Ensemble size analysis shows that increasing the number of ensemble members generally enhances the prediction skill, with 24 members being sufficient for most regions and lead times. However, further increasing the ensemble size is essential to improve the prediction skill at a 6-month lead time. Meanwhile, we also indicate that ws10m can be used as a proxy in evaluating seasonal prediction of wind resources over most regions, while direct seasonal prediction of wind power density is more effective for northern South America. The high seasonal prediction skill of SIDRI-ESS V1.0 highlights its potential for providing valuable seasonal climate prediction services to the wind power industry.

Suggested Citation

  • Zixiang Yan & Wen Zhou & Jinxiao Li & Xuedan Zhu & Yuxin Zang & Liuyi Zhang, 2024. "Skillful Seasonal Prediction of Global Onshore Wind Resources in SIDRI-ESS V1.0," Sustainability, MDPI, vol. 16(17), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7721-:d:1471869
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    References listed on IDEAS

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    1. Brayshaw, David James & Troccoli, Alberto & Fordham, Rachael & Methven, John, 2011. "The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK," Renewable Energy, Elsevier, vol. 36(8), pages 2087-2096.
    2. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    3. Bonou, Alexandra & Laurent, Alexis & Olsen, Stig I., 2016. "Life cycle assessment of onshore and offshore wind energy-from theory to application," Applied Energy, Elsevier, vol. 180(C), pages 327-337.
    4. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    5. Roulston, M.S. & Kaplan, D.T. & Hardenberg, J. & Smith, L.A., 2003. "Using medium-range weather forcasts to improve the value of wind energy production," Renewable Energy, Elsevier, vol. 28(4), pages 585-602.
    6. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    7. Zhao, Jing & Guo, Yiyi & Lin, Yihua & Zhao, Zhiyuan & Guo, Zhenhai, 2024. "A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling," Energy, Elsevier, vol. 302(C).
    8. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
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