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Estimation of monthly global solar irradiation using the Hargreaves–Samani model and an artificial neural network for the state of Alagoas in northeastern Brazil

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Abstract

The monthly global solar irradiation (H g) was estimated using the Hargreaves–Samani model (HS) with three different approaches for determining the k r coefficient and using an artificial neural network (ANN). The data consisted of long-term climate series measured at eight conventional meteorological stations in the state of Alagoas and its borders in northeastern Brazil. The approaches to determine the k r coefficient of the HS model included (i) the method proposed by Hargreaves (1994) (0.190 and 0.162 for coastal and interior regions, respectively), (ii) a method analogous to the previous except with altitude correction, and (iii) k r fitted with local climatic data. A new spatial interpolation method is also proposed to determine k r as a function of geographical coordinates and altitude. The fitted local values of k r (0.168–0.179 and 0.189–0.231 for interior and coastal stations, respectively) exhibited a strong dependence (r 2 = 0.81) on latitude, longitude, and altitude. The estimates of H g obtained with the HS model using fitted local values of k r and those using the ANN were similar (determination coefficient - r 2 = 0.75 and Willmontt agreement coefficient - d = 0.93) and better than those from the HS model using an altitude-corrected k r (r 2 = 0.68 and d = 0.90) or the values proposed by Hargreaves (1994) (r 2 = 0.57 and d = 0.85). The estimates of H g were less accurate and precise for the coastal stations, where cloudiness and humidity are high and the thermal amplitude is small.

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Authors and Affiliations

  1. Departamento de Ciências Ambientais, Instituto de Florestas, Universidade Federal Rural do Rio de Janeiro, Cidade Universitária, Rod BR 465, km 7, CEP 23890-000, Seropédica, Rio de Janeiro, Brazil

    Gustavo Bastos Lyra & José Francisco Oliveira-Júnior

  2. Departamento de Ciências Florestais e da Madeira, Centro de Ciências Agrárias, Universidade Federal do Espírito Santo, 29.550-000, Jerônimo Monteiro, Espírito Santo, Brazil

    Sidney Sára Zanetti

  3. Departamento de Engenharia Agrícola e do Meio Ambiente, Escola de Engenharia, Universidade Federal Fluminense, 24.210-240, Niterói, Rio de Janeiro, Brazil

    Anderson Amorim Rocha Santos

  4. Laboratório de Agrometeorologia e Radiometria Solar, Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Campus A.C. Simões, BR 104-Norte, km 97, Tabuleiro dos Martins, 57.072-970, Maceió, Alagoas, Brazil

    José Leonaldo de Souza & Marco Antônio Maringolo Lemes

  5. Centro de Ciências Agrárias, Universidade Federal de Alagoas, Campus Delza Gítai, BR 104, km 85, s/n, 57.100-000, Rio Largo, Alagoas, Brazil

    Guilherme Bastos Lyra

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  1. Gustavo Bastos Lyra
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Correspondence to Gustavo Bastos Lyra.

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Lyra, G.B., Zanetti, S.S., Santos, A.A.R. et al. Estimation of monthly global solar irradiation using the Hargreaves–Samani model and an artificial neural network for the state of Alagoas in northeastern Brazil. Theor Appl Climatol 125, 743–756 (2016). https://doi.org/10.1007/s00704-015-1541-8

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