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Using artificial neural networks to estimate solar radiation in Kuwait

Author

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  • Bou-Rabee, Mohammed
  • Sulaiman, Shaharin A.
  • Saleh, Magdy Saad
  • Marafi, Suhaila

Abstract

It is an aim of the Kuwaiti government to achieve more penetration levels of renewable energy sources into the national electric grid. Among the various available renewable energy alternatives, solar power generation imposes itself as the most feasible and reasonable solution for a greener Kuwait. In Kuwait, hours of sunshine range between 7h per day in December and 11per day in August. On average, there are about 3347 sunshine hours per year, which make it an ideal place for massive solar energy generation. This massive potential for solar energy will reduce Kuwait's dependency on oil and other fossil fuels and lead to a more secure power supply, a modernized Kuwaiti electric network, more job vacancies, and a dramatically cleaner environment. It is essential to quantify the amounts of solar radiation recorded during the past few years and to make projections for the future. In this paper, a model forecaster for the daily average solar radiation in Kuwait has been developed. The forecasting model is based on artificial neural networks that are able to cope with nonlinear data. Actual data from five different Kuwaiti sites were used as training/testing data while developing the model. The developed forecaster is intended to help country officials, prospective investors, and power system engineers choose locations for solar installation and assess the techno-economic merits of large-scale solar energy integration.

Suggested Citation

  • Bou-Rabee, Mohammed & Sulaiman, Shaharin A. & Saleh, Magdy Saad & Marafi, Suhaila, 2017. "Using artificial neural networks to estimate solar radiation in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 434-438.
    • Handle: RePEc:eee:rensus:v:72:y:2017:i:c:p:434-438
    DOI: 10.1016/j.rser.2017.01.013
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    Citations

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    Cited by:

    1. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    2. Leidy Gutiérrez & Julian Patiño & Eduardo Duque-Grisales, 2021. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction," Energies, MDPI, vol. 14(15), pages 1-16, July.
    3. Muzhou Hou & Tianle Zhang & Futian Weng & Mumtaz Ali & Nadhir Al-Ansari & Zaher Mundher Yaseen, 2018. "Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 11(12), pages 1-19, December.
    4. Salcedo-Sanz, Sancho & Deo, Ravinesh C. & Cornejo-Bueno, Laura & Camacho-Gómez, Carlos & Ghimire, Sujan, 2018. "An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia," Applied Energy, Elsevier, vol. 209(C), pages 79-94.
    5. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    6. Hai Tao & Isa Ebtehaj & Hossein Bonakdari & Salim Heddam & Cyril Voyant & Nadhir Al-Ansari & Ravinesh Deo & Zaher Mundher Yaseen, 2019. "Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme," Energies, MDPI, vol. 12(7), pages 1-24, April.
    7. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    8. Preeti Verma & Sunil Patil, 2023. "A Machine Learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images," Annals of Data Science, Springer, vol. 10(4), pages 907-932, August.
    9. AL-Rousan, Nadia & Isa, Nor Ashidi Mat & Desa, Mohd Khairunaz Mat, 2018. "Advances in solar photovoltaic tracking systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2548-2569.
    10. Al-Dousari, Ali & Al-Nassar, Waleed & Al-Hemoud, Ali & Alsaleh, Abeer & Ramadan, Ashraf & Al-Dousari, Noor & Ahmed, Modi, 2019. "Solar and wind energy: Challenges and solutions in desert regions," Energy, Elsevier, vol. 176(C), pages 184-194.
    11. Du, Bin & Lund, Peter D. & Wang, Jun, 2021. "Combining CFD and artificial neural network techniques to predict the thermal performance of all-glass straight evacuated tube solar collector," Energy, Elsevier, vol. 220(C).
    12. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.

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