Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection
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DOI: 10.1016/j.apenergy.2023.121775
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- Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
- Cheng, Hsu-Yung, 2017. "Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting," Renewable Energy, Elsevier, vol. 104(C), pages 281-289.
- Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
- 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.
- 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).
- David, Mathieu & Luis, Mazorra Aguiar & Lauret, Philippe, 2018. "Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data," International Journal of Forecasting, Elsevier, vol. 34(3), pages 529-547.
- van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
- Kaur, Amanpreet & Nonnenmacher, Lukas & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2016. "Benefits of solar forecasting for energy imbalance markets," Renewable Energy, Elsevier, vol. 86(C), pages 819-830.
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Keywords
Solar energy; Spatiotemporal forecast; Probabilistic forecast; Scale-dependent; Renewable energy; Solar radiation;All these keywords.
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