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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, 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, 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.
    3. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
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    Cited by:

    1. Tingting Zhu & Yiren Guo & Cong Wang & Chao Ni, 2020. "Inter-Hour Forecast of Solar Radiation Based on the Structural Equation Model and Ensemble Model," Energies, MDPI, vol. 13(17), pages 1-15, September.
    2. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    3. Zhenyu Wang & Cuixia Tian & Qibing Zhu & Min Huang, 2018. "Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition," Energies, MDPI, vol. 11(1), pages 1-21, January.

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