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Reliability Predictors for Solar Irradiance Satellite-Based Forecast

Author

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  • Sylvain Cros

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

  • Jordi Badosa

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

  • André Szantaï

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

  • Martial Haeffelin

    (LMD/IPSL, École Polytechnique, IP Paris, Sorbonne Université, ENS, PSL University, CNRS, 91128 Palaiseau, France)

Abstract

The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index ( K c ), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and K c . Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed K c in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking ) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods.

Suggested Citation

  • Sylvain Cros & Jordi Badosa & André Szantaï & Martial Haeffelin, 2020. "Reliability Predictors for Solar Irradiance Satellite-Based Forecast," Energies, MDPI, vol. 13(21), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5566-:d:433975
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    References listed on IDEAS

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    Cited by:

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    2. Thomas Carrière & Rodrigo Amaro e Silva & Fuqiang Zhuang & Yves-Marie Saint-Drenan & Philippe Blanc, 2021. "A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors," Energies, MDPI, vol. 14(16), pages 1-19, August.
    3. Myeongchan Oh & Chang Ki Kim & Boyoung Kim & Changyeol Yun & Yong-Heack Kang & Hyun-Goo Kim, 2021. "Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery," Energies, MDPI, vol. 14(8), pages 1-18, April.
    4. Paletta, Quentin & Hu, Anthony & Arbod, Guillaume & Lasenby, Joan, 2022. "ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy," Applied Energy, Elsevier, vol. 326(C).
    5. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).

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