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Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks

Author

Listed:
  • Ali Kamil Gumar

    (Department of Mechatronics Engineering, Faculty of Engineering, Karabuk University, 78050 Karabuk, Turkey)

  • Funda Demir

    (Department of Mechatronics Engineering, Faculty of Engineering, Karabuk University, 78050 Karabuk, Turkey)

Abstract

Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony.

Suggested Citation

  • Ali Kamil Gumar & Funda Demir, 2022. "Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks," Energies, MDPI, vol. 15(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8669-:d:977182
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    References listed on IDEAS

    as
    1. Mariz B. Arias & Sungwoo Bae, 2021. "Solar Photovoltaic Power Prediction Using Big Data Tools," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    2. Tong Xin & Guolai Yang & Fengjie Xu & Quanzhao Sun & Alexandi Minak, 2021. "Modeling, Simulation and Uncertain Optimization of the Gun Engraving System," Mathematics, MDPI, vol. 9(4), pages 1-25, February.
    3. Mao Yang & Xin Huang, 2018. "An Evaluation Method of the Photovoltaic Power Prediction Quality," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-9, March.
    4. Hongze Li & Sen Guo & Huiru Zhao & Chenbo Su & Bao Wang, 2012. "Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 5(11), pages 1-16, November.
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