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Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan

Author

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  • Faisal Nawab

    (Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
    Department of Renewable Energy, Khushal Khan Khattak University, Karak 27200, Pakistan)

  • Ag Sufiyan Abd Hamid

    (Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia)

  • Ali Alwaeli

    (Engineering Department, American University of Iraq, Sulaimani, Kurdistan Region, Sulaimani 46001, Iraq)

  • Muhammad Arif

    (Center for Advanced Studies in Energy, University of Engineering and Technology, Peshawar 25120, Pakistan)

  • Mohd Faizal Fauzan

    (Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Adnan Ibrahim

    (Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

Abstract

Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/ m 2 / d , and 0.01 kWh/ m 2 / d . The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/ m 2 / d , and 0.01 kWh/m 2 /d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/ m 2 / d , and 0.13 kWh/ m 2 / d , while the reduction for yearly was 91.6%, 0.11 kWh/ m 2 / d , 0.2 kWh/ m 2 / d . The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/ m 2 / d , and 0.16 kWh/ m 2 / d , while the yearly values were 0.47%, 0.18 kWh/ m 2 / d , and 0.004 kWh/ m 2 / d .

Suggested Citation

  • Faisal Nawab & Ag Sufiyan Abd Hamid & Ali Alwaeli & Muhammad Arif & Mohd Faizal Fauzan & Adnan Ibrahim, 2022. "Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7945-:d:851636
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    References listed on IDEAS

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    1. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    2. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    3. 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.
    4. Tahir, Zia ul Rehman & Azhar, Muhammad & Blanc, Philippe & Asim, Muhammad & Imran, Shahid & Hayat, Nasir & Shahid, Hamza & Ali, Hasnain, 2020. "The evaluation of reanalysis and analysis products of solar radiation for Sindh province, Pakistan," Renewable Energy, Elsevier, vol. 145(C), pages 347-362.
    5. Mubiru, J., 2008. "Predicting total solar irradiation values using artificial neural networks," Renewable Energy, Elsevier, vol. 33(10), pages 2329-2332.
    6. Toghraie, Davood & Sina, Nima & Jolfaei, Niyusha Adavoodi & Hajian, Mehdi & Afrand, Masoud, 2019. "Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    7. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.
    8. Tahir, Z.R. & Asim, Muhammad, 2018. "Surface measured solar radiation data and solar energy resource assessment of Pakistan: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2839-2861.
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    3. Faisal Nawab & Ag Sufiyan Abd Hamid & Muhammad Arif & Tufial A. Khan & Amir Naveed & Muhammad Sadiq & Sahibzada Imad Ud din & Adnan Ibrahim, 2022. "Solar–Biogas Microgrid: A Strategy for the Sustainable Development of Rural Communities in Pakistan," Sustainability, MDPI, vol. 14(18), pages 1-15, September.

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