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A statistical approach for sub-hourly solar radiation reconstruction

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  • Zhang, Xiongwen

Abstract

This paper proposes a computational-statistics based approach for solar radiation reconstruction at sub-hourly intervals. A dimensionless form of stochastic variable, V, which is defined as the difference between the theoretical global solar radiation in clear-sky conditions and the actual solar radiation, normalized by the clear-sky global solar radiation, is introduced and adopted in this work. The probability density function of V is calculated from historical data using a Gaussian kernel density estimator. With the developed model, the only input information required for the reconstruction procedure is the cloud condition of the sky (i.e., fair, partly cloudy, overcast, and rain/snow etc.). A case study in simulating solar radiation in Singapore is conducted to validate the accuracy of the model. The calculated results agree well with the measured data. The normalized root mean square error (NRMSE) is on average 23.4% and 7.2% for the one-minute temporal resolution and hourly integral values, respectively.

Suggested Citation

  • Zhang, Xiongwen, 2014. "A statistical approach for sub-hourly solar radiation reconstruction," Renewable Energy, Elsevier, vol. 71(C), pages 307-314.
    • Handle: RePEc:eee:renene:v:71:y:2014:i:c:p:307-314
    DOI: 10.1016/j.renene.2014.05.038
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    References listed on IDEAS

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    1. Jingying Fu & Dong Jiang & Yaohuan Huang & Dafang Zhuang & Yong Wang, 2013. "A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data," Energies, MDPI, vol. 6(6), pages 1-15, June.
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    Cited by:

    1. Dong Liang & Long Ma & Peng Wang & Yuanxia Li & Yiping Luo, 2024. "Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution," Energies, MDPI, vol. 17(10), pages 1-16, May.
    2. Chen, Zhang & Yiliang, Xie & Hongxia, Zhang & Yujie, Gu & Xiongwen, Zhang, 2023. "Optimal design and performance assessment for a solar powered electricity, heating and hydrogen integrated energy system," Energy, Elsevier, vol. 262(PA).
    3. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    4. Su, Gang & Zhang, Shuangyang & Hu, Mengru & Yao, Wanxiang & Li, Ziwei & Xi, Yue, 2022. "The modified layer-by-layer weakening solar radiation models based on relative humidity and air quality index," Energy, Elsevier, vol. 239(PE).
    5. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2016. "Online and batch methods for solar radiation forecast under asymmetric cost functions," Renewable Energy, Elsevier, vol. 91(C), pages 397-408.
    6. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2018. "Parametric methods for probabilistic forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 129(PA), pages 666-676.
    7. Bouabdallah, A. & Olivier, J.C. & Bourguet, S. & Machmoum, M. & Schaeffer, E., 2015. "Safe sizing methodology applied to a standalone photovoltaic system," Renewable Energy, Elsevier, vol. 80(C), pages 266-274.
    8. 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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