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<p>This aim of the study was to model monthly mean global solar radiation (KJ m<sup>−2</sup>) using hourly data from six locations in the State of Alagoas, located in eastern northeastern Brazil (ENEB). Seven probability distribution function (PDF) models were fitted and evaluated based on statistical indicators such as deviation from means (KJ m<sup>−2</sup>), root mean square deviation (RMSE, KJ m<sup>−2</sup>), mean absolute error (MAE, KJ m<sup>−2</sup>), and coefficient of determination (<i>R</i><sup>2</sup>). The hourly data were collected between 2008 and 2016 from INMET’s automatic meteorological stations (EMA) in the coast (three), agreste (2), and sertão (1) climatic mesoregions. The best adjustments of the PDF were GEV (Arapiraca, Pão de Açúcar, and Palmeira dos Índios), Logistic (São Luiz do Quitunde), and EV (Maceió). These adjusted PDFs are essential for better assessing the variability of global solar radiation in the ENEB and for future use of solar energy as an energy matrix in the region, which has the worst socioeconomic indicators and high social vulnerability.</p>

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Statistical modeling of global solar radiation in East and Northeast Brazil

  • 期刊论文

  • 发表日期：

    2025年7月17日

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  • Amaury de Souza,
  • Raquel Soares Casaes Nunes,
  • Deniz Özonur,
  • José Francisco de Oliveira-Júnior,
  • Ivana Pobocikova,
  • Marcel Carvalho Abreu,
  • Elias Silva de Medeiros

摘要

This aim of the study was to model monthly mean global solar radiation (KJ m−2) using hourly data from six locations in the State of Alagoas, located in eastern northeastern Brazil (ENEB). Seven probability distribution function (PDF) models were fitted and evaluated based on statistical indicators such as deviation from means (KJ m−2), root mean square deviation (RMSE, KJ m−2), mean absolute error (MAE, KJ m−2), and coefficient of determination (R2). The hourly data were collected between 2008 and 2016 from INMET’s automatic meteorological stations (EMA) in the coast (three), agreste (2), and sertão (1) climatic mesoregions. The best adjustments of the PDF were GEV (Arapiraca, Pão de Açúcar, and Palmeira dos Índios), Logistic (São Luiz do Quitunde), and EV (Maceió). These adjusted PDFs are essential for better assessing the variability of global solar radiation in the ENEB and for future use of solar energy as an energy matrix in the region, which has the worst socioeconomic indicators and high social vulnerability.