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An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records

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  • Huang, Jing
  • Troccoli, Alberto
  • Coppin, Peter

Abstract

Temporal solar variability significantly affects the integration of solar power systems into the grid. It is thus essential to predict temporal solar variability, particularly given the increasing popularity of solar power generation globally. In this paper, the daily variability of solar irradiance at four sites across Australia is quantified using observed time series of global horizontal irradiance for 2003–2012. It is shown that the daily variability strongly depends on sky clearness with generally low values under a clear or overcast condition and high values under an intermittent cloudiness condition. Various statistical techniques are adopted to model the daily variability using meteorological variables selected from the ERA-Interim reanalysis as predictors. The nonlinear regression technique (i.e. random forest) is demonstrated to perform the best while the performance of the simple analog method is only slightly worse. Among the four sites, Alice Springs has the lowest daily variability index on average and Rockhampton has the highest daily variability index on average. The modelling results of the four sites produced by random forest have a correlation coefficient of above 0.7 and a median relative error around 40%. While the approach of statistical downscaling from a large spatial domain has been applied for other problems, it is shown in this study that it generally suffices to use only the predictors at a single near point for the problem of solar variability. The relative importance of the involved meteorological variables and the effects of clearness on the modelling of the daily variability are also explored.

Suggested Citation

  • Huang, Jing & Troccoli, Alberto & Coppin, Peter, 2014. "An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records," Renewable Energy, Elsevier, vol. 72(C), pages 195-202.
    • Handle: RePEc:eee:renene:v:72:y:2014:i:c:p:195-202
    DOI: 10.1016/j.renene.2014.07.015
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    Cited by:

    1. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    2. Sunghyeon Choi & Jin Hur, 2020. "An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting," Energies, MDPI, vol. 13(6), pages 1-16, March.
    3. 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.
    4. Abhnil Amtesh Prasad & Merlinde Kay, 2020. "Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar," Energies, MDPI, vol. 13(2), pages 1-22, January.
    5. Hartmann, Bálint, 2020. "Comparing various solar irradiance categorization methods – A critique on robustness," Renewable Energy, Elsevier, vol. 154(C), pages 661-671.
    6. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    7. Cecilia Martinez-Castillo & Gonzalo Astray & Juan Carlos Mejuto, 2021. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models," Energies, MDPI, vol. 14(8), pages 1-16, April.
    8. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    9. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
    10. Ming Meng & Chenge Song, 2020. "Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter," Sustainability, MDPI, vol. 12(6), pages 1-17, March.

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