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Comparing various solar irradiance categorization methods – A critique on robustness

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  • Hartmann, Bálint

Abstract

Traditional ways of planning and operation of electricity networks have been challenged lately by the spread of variable renewable energy sources, especially solar photovoltaics, and the need for better forecasting has increased interest in various solutions. Categorization of solar irradiance data, as one of the earliest applied techniques, is a frequently discussed topic in the literature, but the efficiency of different methods may be significantly variable. The aim of this paper is to compare various categorization methods using a one-year-long solar irradiance dataset and reflect on their inefficiencies and the need for more timely solutions.

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  • Hartmann, Bálint, 2020. "Comparing various solar irradiance categorization methods – A critique on robustness," Renewable Energy, Elsevier, vol. 154(C), pages 661-671.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:661-671
    DOI: 10.1016/j.renene.2020.03.055
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    References listed on IDEAS

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    Cited by:

    1. Hassan, Muhammed A. & Abubakr, Mohamed & Khalil, Adel, 2021. "A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals," Renewable Energy, Elsevier, vol. 167(C), pages 613-628.
    2. Zeineb Behi & Kelvin Tsun Wai Ng & Amy Richter & Nima Karimi & Abhijeet Ghosh & Lei Zhang, 2022. "Exploring the untapped potential of solar photovoltaic energy at a smart campus: Shadow and cloud analyses," Energy & Environment, , vol. 33(3), pages 511-526, May.
    3. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.

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