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An Ensemble Approach for Intra-Hour Forecasting of Solar Resource

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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

Solar resource forecasting is an essential step towards smart management of power grids. This study aims to increase the performance of intra-hour forecasts. For this, a novel ensemble model, combining statistical extrapolation of time-series measurements with models based on machine learning and all-sky imagery, is proposed. This study is conducted with high-quality data and high-resolution sky images recorded on the Solar Platform of the West University of Timisoara, Romania. Atmospheric factors that contribute to improving or reducing the quality of forecasts are discussed. Generally, the statistical models gain a small skill score across all forecast horizons (5 to 30 min). The machine-learning-based methods perform best at smaller forecast horizons (less than 15 min), while the all-sky-imagery-based model performs best at larger forecast horizons. Overall, for forecast horizons between 10 and 30 min, the weighted forecast ensemble with frozen coefficients achieves a skill score between 15 and 20%.

Suggested Citation

  • Sergiu-Mihai Hategan & Nicoleta Stefu & Marius Paulescu, 2023. "An Ensemble Approach for Intra-Hour Forecasting of Solar Resource," Energies, MDPI, vol. 16(18), pages 1-12, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6608-:d:1239431
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    References listed on IDEAS

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    1. Shab Gbémou & Julien Eynard & Stéphane Thil & Emmanuel Guillot & Stéphane Grieu, 2021. "A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting," Energies, MDPI, vol. 14(11), pages 1-23, May.
    2. Paulescu, Marius & Paulescu, Eugenia, 2019. "Short-term forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 143(C), pages 985-994.
    3. Michał Musiał & Lech Lichołai & Dušan Katunský, 2023. "Modern Thermal Energy Storage Systems Dedicated to Autonomous Buildings," Energies, MDPI, vol. 16(11), pages 1-28, May.
    4. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    5. Logothetis, Stavros-Andreas & Salamalikis, Vasileios & Wilbert, Stefan & Remund, Jan & Zarzalejo, Luis F. & Xie, Yu & Nouri, Bijan & Ntavelis, Evangelos & Nou, Julien & Hendrikx, Niels & Visser, Lenna, 2022. "Benchmarking of solar irradiance nowcast performance derived from all-sky imagers," Renewable Energy, Elsevier, vol. 199(C), pages 246-261.
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