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Combining physical models to estimate PV power: evaluation and optimal modeling in the solar resource-rich semi-arid Brazilian region

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  • de Medeiros, João Victor Furtado F.
  • Gomes, Emerson Torres Aguiar
  • Alonso-Suárez, Rodrigo
  • de Castro Vilela, Olga

Abstract

Accurate estimation of energy production in photovoltaic power plants is crucial for project feasibility assessment and O&M practices. This study evaluates and analyzes the impact of combining different physical models for PV power modeling, varying different techniques for global horizontal irradiance (GHI) separation, irradiance transposition, and optical, thermal and electrical modeling. High-resolution data collected at 1-min intervals from a 2.5 MWp PV plant located in the Brazilian semi-arid region are used. The PV generation is examined and modeled based on ground-measured GHI, considering a total of 11,340 possible combinations, through seven separation models, nine transposition models, four optical models, nine thermal models, and five electrical models. It is observed that the selection of physical models significantly impacts the estimation, when adopting inaccurate physical models relative differences of 49 % in nMAE and 26 % in nRMSE were evidenced. The models which achieved the best results among the top performers were Starke2 separation model, Perez's transposition model, Martin-Ruiz's optical model, Sandia or Mattei's thermal model and De Soto's electrical model. Additionally, selecting adequate models based on the literature proved to be a good choice for modeling, almost achieving the optimal performance of the best combinations.

Suggested Citation

  • de Medeiros, João Victor Furtado F. & Gomes, Emerson Torres Aguiar & Alonso-Suárez, Rodrigo & de Castro Vilela, Olga, 2026. "Combining physical models to estimate PV power: evaluation and optimal modeling in the solar resource-rich semi-arid Brazilian region," Renewable Energy, Elsevier, vol. 256(PB).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pb:s0960148125016635
    DOI: 10.1016/j.renene.2025.123999
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    References listed on IDEAS

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    1. Badescu, V., 2002. "3D isotropic approximation for solar diffuse irradiance on tilted surfaces," Renewable Energy, Elsevier, vol. 26(2), pages 221-233.
    2. Roberts, Justo José & Mendiburu Zevallos, Andrés A. & Cassula, Agnelo Marotta, 2017. "Assessment of photovoltaic performance models for system simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1104-1123.
    3. Wang, Meng & Peng, Jinqing & Luo, Yimo & Shen, Zhicheng & Yang, Hongxing, 2021. "Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements," Energy, Elsevier, vol. 224(C).
    4. Wattan, Rungrat & Janjai, Serm, 2016. "An investigation of the performance of 14 models for estimating hourly diffuse irradiation on inclined surfaces at tropical sites," Renewable Energy, Elsevier, vol. 93(C), pages 667-674.
    5. Starke, Allan R. & Lemos, Leonardo F.L. & Boland, John & Cardemil, José M. & Colle, Sergio, 2018. "Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction," Renewable Energy, Elsevier, vol. 125(C), pages 472-484.
    6. Balfour, John & Hill, Roger & Walker, Andy & Robinson, Gerald & Gunda, Thushara & Desai, Jal, 2021. "Masking of photovoltaic system performance problems by inverter clipping and other design and operational practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    7. Yujing Sun & Fei Wang & Bo Wang & Qifang Chen & N.A. Engerer & Zengqiang Mi, 2016. "Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems," Energies, MDPI, vol. 10(1), pages 1-20, December.
    8. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    9. Starke, Allan R. & Lemos, Leonardo F.L. & Barni, Cristian M. & Machado, Rubinei D. & Cardemil, José M. & Boland, John & Colle, Sergio, 2021. "Assessing one-minute diffuse fraction models based on worldwide climate features," Renewable Energy, Elsevier, vol. 177(C), pages 700-714.
    10. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
    11. Arias-Rosales, Andrés & LeDuc, Philip R., 2020. "Comparing View Factor modeling frameworks for the estimation of incident solar energy," Applied Energy, Elsevier, vol. 277(C).
    12. Amal Bouich & Inmaculada Guaita Pradas & Mehwish Aziz Khan & Yousaf Hameed Khattak, 2023. "Opportunities, Challenges, and Future Prospects of the Solar Cell Market," Sustainability, MDPI, vol. 15(21), pages 1-15, October.
    13. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    14. Martin Hofmann & Gunther Seckmeyer, 2017. "Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems," Energies, MDPI, vol. 10(10), pages 1-24, September.
    15. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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