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Solar Radiation Forecasting, Accounting for Daily Variability

Author

Listed:
  • Roberto Langella

    () (Department of Industrial and Information Engineering, Second University of Naples via Roma n. 29, Aversa 81031, Italy)

  • Daniela Proto

    () (Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio, n. 21, Napoli 80125, Italy)

  • Alfredo Testa

    () (Department of Industrial and Information Engineering, Second University of Naples via Roma n. 29, Aversa 81031, Italy)

Abstract

Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters’ data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons.

Suggested Citation

  • Roberto Langella & Daniela Proto & Alfredo Testa, 2016. "Solar Radiation Forecasting, Accounting for Daily Variability," Energies, MDPI, Open Access Journal, vol. 9(3), pages 1-17, March.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:200-:d:65780
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    References listed on IDEAS

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    1. Simone Sperati & Stefano Alessandrini & Pierre Pinson & George Kariniotakis, 2015. "The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation," Energies, MDPI, Open Access Journal, vol. 8(9), pages 1-26, September.
    2. Tovar, J & Olmo, F.J & Batlles, F.J & Alados-Arboledas, L, 2001. "Dependence of one-minute global irradiance probability density distributions on hourly irradiation," Energy, Elsevier, vol. 26(7), pages 659-668.
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    Cited by:

    1. repec:gam:jeners:v:11:y:2018:i:1:p:68-:d:124987 is not listed on IDEAS

    More about this item

    Keywords

    solar radiation; solar irradiance; daily variability; instantaneous variability; statistical methods; parametric distributions; time series generation; forecasting;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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