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Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks

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  • Lima, Francisco J.L.
  • Martins, Fernando R.
  • Pereira, Enio B.
  • Lorenz, Elke
  • Heinemann, Detlev

Abstract

There has been a growing demand on energy sector for short-term predictions of energy resources to support the planning and management of electricity generation and distribution systems. The purpose of this work is establishing a methodology to produce solar irradiation forecasts for the Brazilian Northeastern region by using Weather Research and Forecasting Model (WRF) combined with a statistical post-processing method. The 24 h solar irradiance forecasts were obtained using the WRF model. In order to reduce uncertainties, a cluster analysis technique was employed to select areas presenting similar climate features. Comparison analysis between WRF model outputs and observational data were performed to evaluate the model skill in forecasting surface solar irradiance. Next, model-derived short-term solar irradiance forecasts from the WRF outputs were refined by using an artificial neural networks (ANNs) technique. The output variables of the WRF model representing the forecasted atmospheric conditions were used as predictors by ANNs, adjusted to calculate the solar radiation incident for the entire Brazilian Northeastern (NEB) (which was divided into four homogeneous regions, defined by the Ward method). The data used in this study was from rainy and dry seasons between 2009 and 2011. Several predictors were tested to adjust and simulate the ANNs. We found the best ANN architecture and a group of 10 predictors, in which a deeper analyzes were carried out, including performance evaluation for Fall and Spring of 2011 (rainy and dry season in NEB, mainly in the northern section). There was a significant improvement of the WRF model forecasts when adjusted by the ANNs, yielding lower bias and RMSE, and an increase in the correlation coefficient.

Suggested Citation

  • Lima, Francisco J.L. & Martins, Fernando R. & Pereira, Enio B. & Lorenz, Elke & Heinemann, Detlev, 2016. "Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 807-818.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:807-818
    DOI: 10.1016/j.renene.2015.11.005
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    1. Martins, Fernando Ramos & Pereira, Enio Bueno, 2011. "Enhancing information for solar and wind energy technology deployment in Brazil," Energy Policy, Elsevier, vol. 39(7), pages 4378-4390, July.
    2. Martins, F.R. & Rüther, R. & Pereira, E.B. & Abreu, S.L., 2008. "Solar energy scenarios in Brazil. Part two: Photovoltaics applications," Energy Policy, Elsevier, vol. 36(8), pages 2855-2867, August.
    3. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    4. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    5. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    6. Dos Santos, Cícero Manoel & De Souza, José Leonaldo & Ferreira Junior, Ricardo Araujo & Tiba, Chigueru & de Melo, Rinaldo Oliveira & Lyra, Gustavo Bastos & Teodoro, Iêdo & Lyra, Guilherme Bastos & Lem, 2014. "On modeling global solar irradiation using air temperature for Alagoas State, Northeastern Brazil," Energy, Elsevier, vol. 71(C), pages 388-398.
    7. Martins, F.R. & Pereira, E.B. & Silva, S.A.B. & Abreu, S.L. & Colle, Sergio, 2008. "Solar energy scenarios in Brazil, Part one: Resource assessment," Energy Policy, Elsevier, vol. 36(8), pages 2843-2854, August.
    8. Tiba, Chigueru, 2001. "Solar radiation in the Brazilian Northeast," Renewable Energy, Elsevier, vol. 22(4), pages 565-578.
    9. Martins, F.R. & Abreu, S.L. & Pereira, E.B., 2012. "Scenarios for solar thermal energy applications in Brazil," Energy Policy, Elsevier, vol. 48(C), pages 640-649.
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