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Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model

Author

Listed:
  • Elham Alzain

    (Applied College, King Faisal University, Alahsa 31982, Saudi Arabia)

  • Shaha Al-Otaibi

    (Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Theyazn H. H. Aldhyani

    (Applied College, King Faisal University, Alahsa 31982, Saudi Arabia)

  • Ali Saleh Alshebami

    (Applied College, King Faisal University, Alahsa 31982, Saudi Arabia)

  • Mohammed Amin Almaiah

    (Department of Computer Networks and Communications, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
    Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan)

  • Mukti E. Jadhav

    (Department of Computer Science, Shri Shivaji Science & Arts College, Chikhli Dist., Buldana 443201, India)

Abstract

Photovoltaic (PV) power production systems throughout the world struggle with inconsistency in the distribution of PV generation. Accurate PV power forecasting is essential for grid-connected PV systems in case the surrounding environmental conditions experience unfavourable shifts. PV power production forecasting requires the consideration of critical elements, such as grid energy management, grid operation and scheduling. In the present investigation, multilayer perceptron and adaptive network-based fuzzy inference system models were used to forecast PV power production. The developed forecasting model was educated using historical data from October 2011 to February 2022. The outputs of the proposed model were checked for accuracy and compared by considering the dataset from a PV power-producing station. Three different error measurements were used—mean square error, root-mean-square error, and Pearson’s correlation coefficient—to determine the robustness of the suggested method. The suggested method was found to provide better results than the most recent and cutting-edge models. The MLP and ANFIS models achieved the highest performance (R = 100%), with less prediction errors (MSE = 1.1116 × 10 −8 ) and (MSE = 1.3521 × 10 −8 ) with respect to MLP and ANFIS models. The study also predicts future PV power generation values using previously collected PV power production data. The ultimate goal of this work is to produce a model predictive control technique to achieve a balance between the supply and demand of energy.

Suggested Citation

  • Elham Alzain & Shaha Al-Otaibi & Theyazn H. H. Aldhyani & Ali Saleh Alshebami & Mohammed Amin Almaiah & Mukti E. Jadhav, 2023. "Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7999-:d:1146555
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    References listed on IDEAS

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