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Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest

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  • Chen, Jiang
  • Zhu, Weining
  • Yu, Qian

Abstract

To reduce carbon emissions, using more solar energy is a feasible solution. Many meteorological-based models can estimate global downward solar radiation (DSR), but they are with limited applications due to the point-based estimation and low temporal resolution. Satellite remote sensing-based models can estimate DSR with better spatial coverage. However, most previous models are restricted to estimate clear-sky or monthly scale DSR at several sites, limiting the solar energy monitoring of nationwide scale. In this study, using high spatiotemporal resolution Geostationary Operational Environmental Satellites (GOES)-16 satellite data, an iterative random forest (RF) model was developed to estimate and map half-hourly DSR at 1-km spatial resolution over the Continental United States (CONUS). The results show that the iterative RF model performed better than multiple linear regression (MLR) and traditional RF models. The accuracy of estimating half-hourly DSR is that R2 = 0.95, root-mean-square-error (RMSE) = 66.92 W/m2, and mean-bias-error (MBE) = 0.06 W/m2. Half-hourly and daily DSR with spatial resolution 1-km over the CONUS were mapped. The GOES-16 estimated DSR showed the similar spatial patterns with the results from the Clouds and the Earth's Radiant Energy System (CERES) DSR product. This study demonstrated the potential of GOES-16 data for mapping DSR over the CONUS, and hence can be further used in solar energy related applications.

Suggested Citation

  • Chen, Jiang & Zhu, Weining & Yu, Qian, 2021. "Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest," Renewable Energy, Elsevier, vol. 178(C), pages 916-929.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:916-929
    DOI: 10.1016/j.renene.2021.06.129
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    1. Deo, Ravinesh C. & Şahin, Mehmet & Adamowski, Jan F. & Mi, Jianchun, 2019. "Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 235-261.
    2. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    3. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    4. Sujan Ghimire & Ravinesh C Deo & Nawin Raj & Jianchun Mi, 2019. "Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction," Energies, MDPI, vol. 12(12), pages 1-39, June.
    5. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    6. Chen, Ji-Long & Liu, Hong-Bin & Wu, Wei & Xie, De-Ti, 2011. "Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study," Renewable Energy, Elsevier, vol. 36(1), pages 413-420.
    7. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    8. Qingtao Zhou & Alejandro Flores & Nancy F Glenn & Reggie Walters & Bangshuai Han, 2017. "A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    9. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
    11. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
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    2. Chen, Shanlin & Li, Chengxi & Xie, Yuying & Li, Mengying, 2023. "Global and direct solar irradiance estimation using deep learning and selected spectral satellite images," Applied Energy, Elsevier, vol. 352(C).
    3. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
    4. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).

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