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Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?

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  • Chu, Yinghao
  • Yang, Dazhi
  • Yu, Hanxin
  • Zhao, Xin
  • Li, Mengying

Abstract

As a crucial component of the model chain, which facilitates irradiance-to-power conversion during solar resource assessment and forecasting, separation modeling continues to draw attention in both academia and industry. However, when evaluating even the best separation model today, one can quickly recognize its limited accuracy compared to other energy meteorology models such as transposition models. The task of separating global horizontal irradiance into diffuse and beam components does not seem soluble by any derivative effort aimed at tweaking the existing semi-physical models. As a result, an appealing alternative is to consider end-to-end data-driven models, which have demonstrated predictive capability in scenarios where the volume of data is substantial and the interaction among variables is complex. This work discusses the separation of 1-min irradiance from a data-driven perspective. In this preliminary study, a total of 10 representative data-driven separation models are developed and compared to the state-of-the-art semi-physical models, using a comprehensive 1-min irradiance database that spans five years and covers numerous climate types. The average error of the data-driven models is found to be 15.2% to 22.6% lower than that of the semi-physical models for training locations and 7.9% to 17.6% lower for completely unseen locations. Data-driven models also have significantly lower standard deviations (up to 87.2% even for completely unseen locations), highlighting their robustness. In addition, this work provides a guideline for choosing between data-driven and semi-physical models based on data availability, application needs, computational resources, interpretability, and model adaptability. Furthermore, the study underscores the challenges in accurately predicting the diffuse fraction using available input features and indicates that the incorporation of additional weather-related variables and domain knowledge could enhance the performance of data-driven separation models.

Suggested Citation

  • Chu, Yinghao & Yang, Dazhi & Yu, Hanxin & Zhao, Xin & Li, Mengying, 2024. "Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017981
    DOI: 10.1016/j.apenergy.2023.122434
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    References listed on IDEAS

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    1. Pedro, Hugo T.C. & Coimbra, Carlos F.M. & David, Mathieu & Lauret, Philippe, 2018. "Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 191-203.
    2. Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    3. Every, Jeremy P. & Li, Li & Dorrell, David G., 2020. "Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations," Renewable Energy, Elsevier, vol. 147(P1), pages 2453-2469.
    4. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    5. Chu, Yinghao & Coimbra, Carlos F.M., 2017. "Short-term probabilistic forecasts for Direct Normal Irradiance," Renewable Energy, Elsevier, vol. 101(C), pages 526-536.
    6. Yagli, Gokhan Mert & Yang, Dazhi & Gandhi, Oktoviano & Srinivasan, Dipti, 2020. "Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?," Applied Energy, Elsevier, vol. 259(C).
    7. Mayer, Martin János & Yang, Dazhi, 2022. "Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    9. Yang, Dazhi & Gu, Yizhan & Mayer, Martin János & Gueymard, Christian A. & Wang, Wenting & Kleissl, Jan & Li, Mengying & Chu, Yinghao & Bright, Jamie M., 2024. "Regime-dependent 1-min irradiance separation model with climatology clustering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    10. Starke, Allan R. & Lemos, Leonardo F.L. & Boland, John & Cardemil, José M. & Colle, Sergio, 2018. "Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction," Renewable Energy, Elsevier, vol. 125(C), pages 472-484.
    11. Anagnostos, D. & Schmidt, T. & Cavadias, S. & Soudris, D. & Poortmans, J. & Catthoor, F., 2019. "A method for detailed, short-term energy yield forecasting of photovoltaic installations," Renewable Energy, Elsevier, vol. 130(C), pages 122-129.
    12. Mayer, Martin János & Yang, Dazhi, 2023. "Calibration of deterministic NWP forecasts and its impact on verification," International Journal of Forecasting, Elsevier, vol. 39(2), pages 981-991.
    13. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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