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Potential of four different machine-learning algorithms in modeling daily global solar radiation

Author

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  • Hassan, Muhammed A.
  • Khalil, A.
  • Kaseb, S.
  • Kassem, M.A.

Abstract

In this study, the potential of different machine-learning algorithms in modeling global horizontal solar irradiation is examined. Multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) and Support Vector Machines (SVM) algorithms are adopted, beside a newly suggested algorithm: decision trees. All models are grouped in four categories: sunshine-, temperature-, meteorological parameters- and day number-based models. All models have been trained, optimized, validated and compared with each other and with old and newly suggested regression models, using high-resolution, highly accurate measured data recorded over Cairo, Egypt, throughout five years, as a case study. Models with best statistical measures of accuracy and best generalization abilities have been recommended after being tested using an independent dataset. The results show that MLP models excel in estimating global irradiation with root mean square error lower than that of best corresponding regression models by 4.75–31.69%, depending on the model category. Followed by ANFIS models (if carefully validated) and SVM models. In addition, the study assesses the ability of decision trees in modeling solar radiation. Despite of their simplicity, the merits of temperature- and day number-based models are demonstrated, with coefficients of determination greater than 85%, to be used in case of unavailability of sunshine records.

Suggested Citation

  • Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Potential of four different machine-learning algorithms in modeling daily global solar radiation," Renewable Energy, Elsevier, vol. 111(C), pages 52-62.
    • Handle: RePEc:eee:renene:v:111:y:2017:i:c:p:52-62
    DOI: 10.1016/j.renene.2017.03.083
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    Citations

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    Cited by:

    1. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
    2. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    3. Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    4. Hassan, Muhammed A. & Al-Ghussain, Loiy & Ahmad, Adnan Darwish & Abubaker, Ahmad M. & Khalil, Adel, 2022. "Aggregated independent forecasters of half-hourly global horizontal irradiance," Renewable Energy, Elsevier, vol. 181(C), pages 365-383.
    5. Hassan, Muhammed A. & Abubakr, Mohamed & Khalil, Adel, 2021. "A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals," Renewable Energy, Elsevier, vol. 167(C), pages 613-628.
    6. Kaood, Amr & Abubakr, Mohamed & Al-Oran, Otabeh & Hassan, Muhammed A., 2021. "Performance analysis and particle swarm optimization of molten salt-based nanofluids in parabolic trough concentrators," Renewable Energy, Elsevier, vol. 177(C), pages 1045-1062.
    7. 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.
    8. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    9. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    10. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2023. "Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods," Energies, MDPI, vol. 16(17), pages 1-30, August.
    11. 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.
    12. Bailek, Nadjem & Bouchouicha, Kada & Hassan, Muhammed A. & Slimani, Abdeldjalil & Jamil, Basharat, 2020. "Implicit regression-based correlations to predict the back temperature of PV modules in the arid region of south Algeria," Renewable Energy, Elsevier, vol. 156(C), pages 57-67.
    13. 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.
    14. Bouchouicha, Kada & Hassan, Muhammed A. & Bailek, Nadjem & Aoun, Nouar, 2019. "Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate," Renewable Energy, Elsevier, vol. 139(C), pages 844-858.
    15. Bikhtiyar Ameen & Heiko Balzter & Claire Jarvis & James Wheeler, 2019. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks," Energies, MDPI, vol. 12(1), pages 1-28, January.
    16. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).
    17. Muhammed A. Hassan & Hindawi Salem & Nadjem Bailek & Ozgur Kisi, 2023. "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

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