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Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study

Author

Listed:
  • Brahim Belmahdi

    (Energetics Laboratory, ETEE, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan 93000, Morocco)

  • Mohamed Louzazni

    (Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, El Jadida 24000, Morocco)

  • Mousa Marzband

    (Electrical Power and Control Systems Research Group, Northumbria University, Ellison Place, Newcastle upon Tyne NE1 8ST, UK
    Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Abdelmajid El Bouardi

    (Energetics Laboratory, ETEE, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan 93000, Morocco)

Abstract

The adequate modeling and estimation of solar radiation plays a vital role in designing solar energy applications. In fact, unnecessary environmental changes result in several problems with the components of solar photovoltaic and affects the energy generation network. Various computational algorithms have been developed over the past decades to improve the efficiency of predicting solar radiation with various input characteristics. This research provides five approaches for forecasting daily global solar radiation (GSR) in two Moroccan cities, Tetouan and Tangier. In this regard, autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), feed forward back propagation neural networks (FFBP), hybrid ARIMA-FFBP, and hybrid ARMA-FFBP were selected to compare and forecast the daily global solar radiation with different combinations of meteorological parameters. In addition, the performance in three approaches has been calculated in terms of the statistical metric correlation coefficient (R 2 ), root means square error (RMSE), stand deviation (σ), the slope of best fit (SBF), legate’s coefficient of efficiency (LCE), and Wilmott’s index of agreement (WIA). The best model is selected by using the computed statistical metric, which is present, and the optimal value. The R 2 of the forecasted ARIMA, ARMA, FFBP, hybrid ARIMA-FFBP, and ARMA-FFBP models is varying between 0.9472% and 0.9931%. The range value of SPE is varying between 0.8435 and 0.9296. The range value of LCE is 0.8954 and 0.9696 and the range value of WIA is 0.9491 and 0.9945. The outcomes show that the hybrid ARIMA–FFBP and hybrid ARMA–FFBP techniques are more effective than other approaches due to the improved correlation coefficient (R 2 ).

Suggested Citation

  • Brahim Belmahdi & Mohamed Louzazni & Mousa Marzband & Abdelmajid El Bouardi, 2023. "Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study," Forecasting, MDPI, vol. 5(1), pages 1-24, January.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:1:p:9-195:d:1048403
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    References listed on IDEAS

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    1. Hassan, Jamal, 2014. "ARIMA and regression models for prediction of daily and monthly clearness index," Renewable Energy, Elsevier, vol. 68(C), pages 421-427.
    2. Bin Shams, Mohamed & Haji, Shaker & Salman, Ali & Abdali, Hussain & Alsaffar, Alaa, 2016. "Time series analysis of Bahrain's first hybrid renewable energy system," Energy, Elsevier, vol. 103(C), pages 1-15.
    3. López, G. & Batlles, F.J. & Tovar-Pescador, J., 2005. "Selection of input parameters to model direct solar irradiance by using artificial neural networks," Energy, Elsevier, vol. 30(9), pages 1675-1684.
    4. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    5. Ma, Tao & Yang, Hongxing & Lu, Lin & Peng, Jinqing, 2015. "Optimal design of an autonomous solar–wind-pumped storage power supply system," Applied Energy, Elsevier, vol. 160(C), pages 728-736.
    6. Sulaiman, M.Yusof & Hlaing oo, W.M. & Wahab, Mahdi Abd. & Sulaiman, Z.Abidin, 1997. "Analysis of residuals in daily solar radiation time series," Renewable Energy, Elsevier, vol. 11(1), pages 97-105.
    7. Ines Sansa & Zina Boussaada & Najiba Mrabet Bellaaj, 2021. "Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX," Energies, MDPI, vol. 14(21), pages 1-26, October.
    8. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    9. Mustafa Jaihuni & Jayanta Kumar Basak & Fawad Khan & Frank Gyan Okyere & Elanchezhian Arulmozhi & Anil Bhujel & Jihoon Park & Lee Deog Hyun & Hyeon Tae Kim, 2020. "A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms," Energies, MDPI, vol. 13(2), pages 1-20, January.
    10. 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.
    11. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
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