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A high-resolution downscaling approach for solar irradiance using statistical parameter matching

Author

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  • Omoyele, Olalekan
  • Hoffmann, Maximilian
  • Weinand, Jann Michael
  • Larrañeta, Miguel
  • Linßen, Jochen
  • Stolten, Detlef

Abstract

The limited intra-hour variability of globally available hourly renewable energy system data leads to inaccuracies in the modeling of renewable energy systems. While sub-hourly data can improve model accuracy, such data are not globally available. The existing approaches to increase the temporal resolution of solar irradiance often rely on site specific measurements or complex models, limiting global scalability. This work, therefore, presents a methodology to increase the temporal resolution of the global horizontal irradiance from 1 h to 1 min using non-dimensional irradiance and parameters matching based on daily irradiance characteristics for arbitrary locations. The methodology is validated using statistical methods and energy system optimization. The hourly annual normalized root mean square error and Kolmogorov-Smirnov Integral range from 5 to 7 % and 0.1–0.7, respectively, for different locations consisting of varying weather conditions. The energy system optimization results of the synthetic data demonstrate superiority in terms of cost and feasibility relative to the average hourly resolution data. The use of synthetic minute resolution data significantly improves the design accuracy of dynamic components such as inverters and storage systems. The globally applicable method, based on Köppen-Geiger classification coverage, will enable more reliable energy systems modeling in the future.

Suggested Citation

  • Omoyele, Olalekan & Hoffmann, Maximilian & Weinand, Jann Michael & Larrañeta, Miguel & Linßen, Jochen & Stolten, Detlef, 2026. "A high-resolution downscaling approach for solar irradiance using statistical parameter matching," Renewable Energy, Elsevier, vol. 256(PI).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pi:s0960148125022153
    DOI: 10.1016/j.renene.2025.124551
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    References listed on IDEAS

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    1. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    2. Antonanzas-Torres, F. & Urraca, R. & Polo, J. & Perpiñán-Lamigueiro, O. & Escobar, R., 2019. "Clear sky solar irradiance models: A review of seventy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 374-387.
    3. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Deane, J.P. & Drayton, G. & Ó Gallachóir, B.P., 2014. "The impact of sub-hourly modelling in power systems with significant levels of renewable generation," Applied Energy, Elsevier, vol. 113(C), pages 152-158.
    5. Irena Balog & Giampaolo Caputo & Domenico Iatauro & Paolo Signoretti & Francesco Spinelli, 2023. "Downscaling of Hourly Climate Data for the Assessment of Building Energy Performance," Sustainability, MDPI, vol. 15(3), pages 1-14, February.
    6. Reno, Matthew J. & Hansen, Clifford W., 2016. "Identification of periods of clear sky irradiance in time series of GHI measurements," Renewable Energy, Elsevier, vol. 90(C), pages 520-531.
    7. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Acord, Brendan & Wang, Peng & Engerer, Nicholas A., 2019. "Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 550-570.
    8. Kittel, Martin & Hobbie, Hannes & Dierstein, Constantin, 2022. "Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 247, pages 1-15.
    9. Martin Hofmann & Gunther Seckmeyer, 2017. "Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems," Energies, MDPI, vol. 10(10), pages 1-24, September.
    10. Salazar, Germán & Gueymard, Christian & Galdino, Janis Bezerra & de Castro Vilela, Olga & Fraidenraich, Naum, 2020. "Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
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