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Generating station-like downward shortwave radiation data by using sky condition-guided model based on ERA5-Land data

Author

Listed:
  • Li, Xuehua
  • Chen, Yunhao
  • Li, Kangning
  • Liu, Xiuyu
  • Gao, Shengjun
  • Ji, Weizhen
  • Cui, Ying

Abstract

Accurate downward shortwave radiation (DSR) data is essential for the utilization of solar energy resources. However, existing various DSR data have certain limitations in applications. Among these, ERA5-Land DSR data is considered a relatively high-quality dataset. Nevertheless, it is reported notable deviations under different sky conditions, resulted from deficiencies in cloud and aerosol simulations. Therefore, this study aims to generate high-quality DSR data based on ERA5-Land data. We propose a sky condition-guided model using deviation characteristics of ERA5-Land DSR across different sky conditions as guidance, incorporating reliable cloud and aerosol parameters to provide atmosphere information. Consequently, station-like DSR data with accuracy close to station data is generated. Our model achieves high accuracy under different sky conditions, with RMSE below 32.19 W/m2. At the individual-station scale, 86.04 % of validation sites exhibit R values exceeding 0.9. At the seasonal scale, R values consistently surpass 0.87. Moreover, our data exhibits better accuracy compared to mainstream DSR datasets. Furthermore, it depicts the spatiotemporal variation of DSR and further reveals the photovoltaic potential in China, with relatively high values in the Tibetan Plateau and northwest China. The station-like DSR data is promising to advance the utilization of solar energy and accelerate the energy transition.

Suggested Citation

  • Li, Xuehua & Chen, Yunhao & Li, Kangning & Liu, Xiuyu & Gao, Shengjun & Ji, Weizhen & Cui, Ying, 2024. "Generating station-like downward shortwave radiation data by using sky condition-guided model based on ERA5-Land data," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224021911
    DOI: 10.1016/j.energy.2024.132417
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    as
    1. 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).
    2. Jerez, S. & Thais, F. & Tobin, I. & Wild, M. & Colette, A. & Yiou, P. & Vautard, R., 2015. "The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1-15.
    3. Neshat, Mehdi & Nezhad, Meysam Majidi & Mirjalili, Seyedali & Garcia, Davide Astiaso & Dahlquist, Erik & Gandomi, Amir H., 2023. "Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy," Energy, Elsevier, vol. 278(C).
    4. Jerez, S. & Tobin, I. & Turco, M. & Jiménez-Guerrero, P. & Vautard, R. & Montávez, J.P., 2019. "Future changes, or lack thereof, in the temporal variability of the combined wind-plus-solar power production in Europe," Renewable Energy, Elsevier, vol. 139(C), pages 251-260.
    5. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    6. Xuan, Zhiwei & Ge, Minghui & Zhao, Chenyang & Li, Yanzhe & Wang, Shixue & Zhao, Yulong, 2024. "Effect of nonuniform solar radiation on the performance of solar thermoelectric generators," Energy, Elsevier, vol. 290(C).
    7. Ermolenko, Boris V. & Ermolenko, Georgy V. & Fetisova, Yulia A. & Proskuryakova, Liliana N., 2017. "Wind and solar PV technical potentials: Measurement methodology and assessments for Russia," Energy, Elsevier, vol. 137(C), pages 1001-1012.
    8. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.
    9. Yadong Lei & Zhili Wang & Deying Wang & Xiaoye Zhang & Huizheng Che & Xu Yue & Chenguang Tian & Junting Zhong & Lifeng Guo & Lei Li & Hao Zhou & Lin Liu & Yangyang Xu, 2023. "Co-benefits of carbon neutrality in enhancing and stabilizing solar and wind energy," Nature Climate Change, Nature, vol. 13(7), pages 693-700, July.
    10. Wang, Yuhan & Zhang, Chu & Fu, Yongyan & Suo, Leiming & Song, Shihao & Peng, Tian & Shahzad Nazir, Muhammad, 2023. "Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm," Energy, Elsevier, vol. 280(C).
    11. Cao, Qimeng & Liu, Yan & Sun, Xue & Yang, Liu, 2022. "Country-level evaluation of solar radiation data sets using ground measurements in China," Energy, Elsevier, vol. 241(C).
    12. Jiang, Hou & Lu, Ning & Qin, Jun & Yao, Ling, 2021. "Hierarchical identification of solar radiation zones in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    13. Nunez Munoz, Maria & Ballantyne, Erica E.F. & Stone, David A., 2022. "Development and evaluation of empirical models for the estimation of hourly horizontal diffuse solar irradiance in the United Kingdom," Energy, Elsevier, vol. 241(C).
    14. Sonia Jerez & Isabelle Tobin & Robert Vautard & Juan Pedro Montávez & Jose María López-Romero & Françoise Thais & Blanka Bartok & Ole Bøssing Christensen & Augustin Colette & Michel Déqué & Grigory Ni, 2015. "The impact of climate change on photovoltaic power generation in Europe," Nature Communications, Nature, vol. 6(1), pages 1-8, December.
    15. Bakirci, Kadir, 2021. "Prediction of diffuse radiation in solar energy applications: Turkey case study and compare with satellite data," Energy, Elsevier, vol. 237(C).
    16. Boilley, Alexandre & Wald, Lucien, 2015. "Comparison between meteorological re-analyses from ERA-Interim and MERRA and measurements of daily solar irradiation at surface," Renewable Energy, Elsevier, vol. 75(C), pages 135-143.
    17. Sarah Feron & Raúl R. Cordero & Alessandro Damiani & Robert B. Jackson, 2021. "Climate change extremes and photovoltaic power output," Nature Sustainability, Nature, vol. 4(3), pages 270-276, March.
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