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Diffuse fraction estimation using the BRL model and relationship of predictors under Chilean, Costa Rican and Australian climatic conditions

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  • Rojas, Redlich García
  • Alvarado, Natalia
  • Boland, John
  • Escobar, Rodrigo
  • Castillejo-Cuberos, Armando

Abstract

In this paper a model is used to estimate the diffuse radiation using the Boland-Ridley-Lauret (BRL) model, which is developed for Chile, Costa Rica, and Australia. Additionally, artificial Direct Normal Irradiance time series are determined from the diffuse fraction results estimated by the model. Both estimates achieve excellent agreement based on the bias and normalized scatter. The relationship between the predictors of the model and the climatic conditions of each case of study, according to the Köppen-Geiger climatic classification was analyzed, which to the best of the authors' knowledge, it's the first study of this kind. An analysis on correlation and statistical significance was carried out between the model predictors and four determining geographic and climatic variables: altitude, latitude, precipitation and temperature. The statistical analysis shows that two of the six predictors correlate with temperature and precipitation and one predictor is correlated with latitude. Therefore, it can be suggested that the BRL model seems largely insensitive to the different regional climatic conditions, nevertheless, evaluation of the effect of the apparent correlation of the respective predictors and microclimatic and geographical variables points to further research in this area considering a wider selection of locations.

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  • Rojas, Redlich García & Alvarado, Natalia & Boland, John & Escobar, Rodrigo & Castillejo-Cuberos, Armando, 2019. "Diffuse fraction estimation using the BRL model and relationship of predictors under Chilean, Costa Rican and Australian climatic conditions," Renewable Energy, Elsevier, vol. 136(C), pages 1091-1106.
    • Handle: RePEc:eee:renene:v:136:y:2019:i:c:p:1091-1106
    DOI: 10.1016/j.renene.2018.09.079
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    1. Del Hoyo, Mirko & Rondanelli, Roberto & Escobar, Rodrigo, 2020. "Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile," Renewable Energy, Elsevier, vol. 148(C), pages 1137-1149.
    2. Sadat, Seyyed Ali & Hoex, Bram & Pearce, Joshua M., 2022. "A Review of the Effects of Haze on Solar Photovoltaic Performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Castillejo-Cuberos, A. & Cardemil, J.M. & Escobar, R., 2024. "Temporal upscaling of solar radiation components using an analytical model for variability modeling," Renewable Energy, Elsevier, vol. 229(C).
    4. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
    5. Nollas, Fernando M. & Salazar, German A. & Gueymard, Christian A., 2023. "Quality control procedure for 1-minute pyranometric measurements of global and shadowband-based diffuse solar irradiance," Renewable Energy, Elsevier, vol. 202(C), pages 40-55.
    6. Castillejo-Cuberos, Armando & Escobar, Rodrigo, 2020. "Understanding solar resource variability: An in-depth analysis, using Chile as a case of study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).

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