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Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records

Author

Listed:
  • Si-Ya Wang

    (College of Water Resources & Civil Engineering, China Agricultural University; Beijing 100083, China)

  • Jun Qiu

    (State Key Laboratory of Hydroscience & Engineering, Tsinghua University, Beijing 100084, China
    State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China)

  • Fang-Fang Li

    (College of Water Resources & Civil Engineering, China Agricultural University; Beijing 100083, China
    State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China)

Abstract

Solar radiation prediction is significant for solar energy utilization. This paper presents hybrid methods following the decomposition-prediction-reconfiguration paradigm using only historical radiation records with different combination of decomposition methods, Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Analysis (WA), and the reconfiguration methods, regression model (RE) and Artificial Neural Network (ANN). The application in west China indicates that these hybrid decomposition-reconfiguration models perform well for monthly prediction, while the comparisons of the daily prediction show that the hybrid EEMD-RE model has a higher degree of fitting and a better prediction effect in long-term prediction of solar radiation intensity, which verifies (1) decomposition of original solar radiation data results in components with regular characteristics; (2) the relationship between the original solar radiation sequence and the derived intrinsic mode functions (IMFs) is linear; and (3) EEMD has strong adaptivity for non-linear and non-stationary series. The proposed hybrid decomposition-reconfiguration models have great application prospect for monthly long-term prediction of solar radiation intensity, especially in the areas where complex climate data is difficult to obtain, and the EEMD-RE model is recommended for the daily long-term prediction.

Suggested Citation

  • Si-Ya Wang & Jun Qiu & Fang-Fang Li, 2018. "Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records," Energies, MDPI, vol. 11(6), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1376-:d:149437
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