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Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

Author

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  • Llinet Benavides Cesar

    (Departamento de Ingeniería Topográfica y Cartográfica, Escuela Técnica Superior de Ingenieros en Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain)

  • Rodrigo Amaro e Silva

    (O.I.E. Centre Observation, Impacts, Energy, MINES ParisTech, PSL Research University, 06904 Paris, France)

  • Miguel Ángel Manso Callejo

    (Departamento de Ingeniería Topográfica y Cartográfica, Escuela Técnica Superior de Ingenieros en Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain)

  • Calimanut-Ionut Cira

    (Departamento de Ingeniería Topográfica y Cartográfica, Escuela Técnica Superior de Ingenieros en Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain)

Abstract

To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.

Suggested Citation

  • Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4341-:d:838328
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    References listed on IDEAS

    as
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    Cited by:

    1. Llinet Benavides Cesar & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira & Ramon Alcarria, 2023. "CyL-GHI: Global Horizontal Irradiance Dataset Containing 18 Years of Refined Data at 30-Min Granularity from 37 Stations Located in Castile and León (Spain)," Data, MDPI, vol. 8(4), pages 1-21, March.

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