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Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data

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  • David, Mathieu
  • Luis, Mazorra Aguiar
  • Lauret, Philippe

Abstract

Accurate solar forecasts are necessary to improve the integration of solar renewables into the energy grid. In recent years, numerous methods have been developed for predicting the solar irradiance or the output of solar renewables. By definition, a forecast is uncertain. Thus, the models developed predict the mean and the associated uncertainty. Comparisons are therefore necessary and useful for assessing the skill and accuracy of these new methods in the field of solar energy.

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  • 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.
    • Handle: RePEc:eee:intfor:v:34:y:2018:i:3:p:529-547
    DOI: 10.1016/j.ijforecast.2018.02.003
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    5. Chen, Yunxiao & Bai, Mingliang & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2023. "Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting," Energy, Elsevier, vol. 284(C).
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    7. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko & Hesham S. Rabayah & Raed M. Abendeh & Rami Alawneh, 2023. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations," Energies, MDPI, vol. 16(13), pages 1-24, June.
    8. 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.
    9. Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
    10. Bastos, Bruno Quaresma & Cyrino Oliveira, Fernando Luiz & Milidiú, Ruy Luiz, 2021. "U-Convolutional model for spatio-temporal wind speed forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 949-970.
    11. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    12. 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).
    13. Xiwen Cui & Shaojun E & Dongxiao Niu & Bosong Chen & Jiaqi Feng, 2021. "Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    14. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).

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