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Calibration of GFS Solar Irradiation Forecasts: A Case Study in Romania

Author

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  • Sergiu-Mihai Hategan

    (Faculty of Physics, West University of Timisoara, V. Parvan 4, 300223 Timisoara, Romania)

  • Nicoleta Stefu

    (Faculty of Physics, West University of Timisoara, V. Parvan 4, 300223 Timisoara, Romania)

  • Marius Paulescu

    (Faculty of Physics, West University of Timisoara, V. Parvan 4, 300223 Timisoara, Romania)

Abstract

Models based on Numerical Weather Prediction (NWP) are widely used for the day-ahead forecast of solar resources. This study is focused on the calibration of the hourly global solar irradiation forecasts provided by the Global Forecast System (GFS), a model from the NWP class. Since the evaluation of GFS raw forecasts sometimes shows a high degree of uncertainty (the relative error exceeding 100%), a procedure for reducing the errors is needed as a prerequisite for engineering applications. In this study, a deep analysis of the error sources in relation to the state of the atmosphere is reported. Of special note is the use of sky imagery in the identification process. Generally, it has been found that the largest errors are determined by the underestimation of cloud coverage. For calibration, a new ensemble forecast is proposed. It combines two machine learning approaches, Support Vector Regression and Multi-Layer Perceptron. In contrast to a typical calibration, the objective function is constructed based on the absolute error instead of the traditional root mean squared error. In terms of normalized root mean squared error, the calibration roughly reduces the uncertainty in hourly global solar irradiation by 16%. The study was conducted with high-quality ground-measured data from the Solar Platform of the West University of Timisoara, Romania. To ensure high accessibility, all the parameters required to run the proposed calibration procedures are provided.

Suggested Citation

  • Sergiu-Mihai Hategan & Nicoleta Stefu & Marius Paulescu, 2023. "Calibration of GFS Solar Irradiation Forecasts: A Case Study in Romania," Energies, MDPI, vol. 16(11), pages 1-11, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4290-:d:1154340
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    References listed on IDEAS

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    1. Ahmed Mazin Majid AL-Qaysi & Altug Bozkurt & Yavuz Ates, 2023. "Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq," Energies, MDPI, vol. 16(6), pages 1-20, March.
    2. Paulescu, Marius & Paulescu, Eugenia, 2019. "Short-term forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 143(C), pages 985-994.
    3. Nonthawat Khortsriwong & Promphak Boonraksa & Terapong Boonraksa & Thipwan Fangsuwannarak & Asada Boonsrirat & Watcharakorn Pinthurat & Boonruang Marungsri, 2023. "Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant," Energies, MDPI, vol. 16(5), pages 1-21, February.
    4. 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.
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