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Estimation of Solar Diffuse Radiation in Chongqing Based on Random Forest

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  • Peihan Wan

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yongjian He

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Chaoyu Zheng

    (Fujian Provincial Climate Center, Fujian Provincial Meteorological Bureau, Fuzhou 350001, China)

  • Jiaxiong Wen

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Zhuting Gu

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

Solar diffuse radiation (DIFRA) is an important component of solar radiation, but current research into the estimation of DIFRA is relatively limited. This study, based on remote sensing data, topographic data, meteorological reanalysis materials, and measured data from radiation observation stations in Chongqing, combined key factors such as the solar elevation angle, water vapor, aerosols, and cloud cover. A high-precision DIFRA estimation model was developed using the random forest algorithm, and a distributed simulation of DIFRA in Chongqing was achieved. The model was validated using 8179 measured data points, demonstrating good predictive capability with a correlation coefficient (R 2 ) of 0.72, a mean absolute error (MAE) of 35.99 W/m 2 , and a root mean square error (RMSE) of 50.46 W/m 2 . Further validation was conducted based on 14 radiation observation stations, with the model demonstrating high stability and applicability across different stations and weather conditions. In particular, the fit was optimal for the model under overcast conditions, with R 2 = 0.70, MAE = 32.20 W/m 2 , and RMSE = 47.51 W/m 2 . The results indicate that the model can be effectively adapted to all weather calculations, providing a scientific basis for assessing and exploiting solar energy resources in complex terrains.

Suggested Citation

  • Peihan Wan & Yongjian He & Chaoyu Zheng & Jiaxiong Wen & Zhuting Gu, 2025. "Estimation of Solar Diffuse Radiation in Chongqing Based on Random Forest," Energies, MDPI, vol. 18(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:836-:d:1588601
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    References listed on IDEAS

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    1. Saioa Etxebarria Berrizbeitia & Eulalia Jadraque Gago & Tariq Muneer, 2020. "Empirical Models for the Estimation of Solar Sky-Diffuse Radiation. A Review and Experimental Analysis," Energies, MDPI, vol. 13(3), pages 1-23, February.
    2. Chih-Chiang Wei & Yen-Chen Yang, 2023. "A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models," Energies, MDPI, vol. 16(23), pages 1-18, November.
    3. Lilla Barancsuk & Veronika Groma & Dalma Günter & János Osán & Bálint Hartmann, 2024. "Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data," Energies, MDPI, vol. 17(2), pages 1-25, January.
    4. Wang, Hong & Sun, Fubao & Wang, Tingting & Liu, Wenbin, 2018. "Estimation of daily and monthly diffuse radiation from measurements of global solar radiation a case study across China," Renewable Energy, Elsevier, vol. 126(C), pages 226-241.
    5. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
    6. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2024. "Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis," Energies, MDPI, vol. 17(17), pages 1-42, August.
    7. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
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