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Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery

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  • Rodríguez-Benítez, Francisco J.
  • López-Cuesta, Miguel
  • Arbizu-Barrena, Clara
  • Fernández-León, María M.
  • Pamos-Ureña, Miguel Á.
  • Tovar-Pescador, Joaquín
  • Santos-Alamillos, Francisco J.
  • Pozo-Vázquez, David

Abstract

This work proposes and evaluates methods for extending the forecasting horizon of all-sky imager (ASI)-based solar radiation nowcasts and estimating the uncertainty of these predictions. In addition, we evaluated procedures for improving the temporal resolution and latency of satellite-imagery-derived solar nowcasts. Based on these contributions, we assessed the reliability of ASIs and satellite-derived solar radiation nowcasts, with 1-min time-resolution and up-to-90-min ahead. The study was conducted in a location in Southern Spain using a set of cloudy days, specifically selected as representative of the most challenging conditions regarding solar radiation nowcasting. The results reveal that the use of ASI-based models provide low benefits compared to the use of satellite-based models for point solar radiation nowcasting. Given the frequency of occurrence of the different sky types in the study area, the results suggest that the use of a simple smart persistence algorithm, in combination with a low-resolution satellite nowcasting model could be an adequate choice, avoiding the challenges associated with the use of ASIs.

Suggested Citation

  • Rodríguez-Benítez, Francisco J. & López-Cuesta, Miguel & Arbizu-Barrena, Clara & Fernández-León, María M. & Pamos-Ureña, Miguel Á. & Tovar-Pescador, Joaquín & Santos-Alamillos, Francisco J. & Pozo-Váz, 2021. "Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery," Applied Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:appene:v:292:y:2021:i:c:s0306261921003354
    DOI: 10.1016/j.apenergy.2021.116838
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    4. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
    5. Garcia, Dário & Liang, Dawei & Almeida, Joana & Catela, Miguel & Costa, Hugo & Tibúrcio, Bruno D. & Guillot, Emmanuel & Vistas, Cláudia R., 2023. "Lowest-threshold solar laser operation under cloudy sky condition," Renewable Energy, Elsevier, vol. 210(C), pages 127-133.
    6. Xinyu Yang & Ying Ji & Xiaoxia Wang & Menghan Niu & Shuijing Long & Jingchao Xie & Yuying Sun, 2023. "Simplified Method for Predicting Hourly Global Solar Radiation Using Extraterrestrial Radiation and Limited Weather Forecast Parameters," Energies, MDPI, vol. 16(7), pages 1-16, April.
    7. Paletta, Quentin & Arbod, Guillaume & Lasenby, Joan, 2023. "Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions," Applied Energy, Elsevier, vol. 336(C).
    8. Eşlik, Ardan Hüseyin & Akarslan, Emre & Hocaoğlu, Fatih Onur, 2022. "Short-term solar radiation forecasting with a novel image processing-based deep learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 1490-1505.
    9. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    10. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
    11. Carpentieri, A. & Folini, D. & Nerini, D. & Pulkkinen, S. & Wild, M. & Meyer, A., 2023. "Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection," Applied Energy, Elsevier, vol. 351(C).
    12. Paletta, Quentin & Hu, Anthony & Arbod, Guillaume & Lasenby, Joan, 2022. "ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy," Applied Energy, Elsevier, vol. 326(C).

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