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Impacts of Artificial Neural Network Training Algorithms on the Accuracy of PV System Voltage and Current Predictions

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  • Aschenaki Altaye
  • Istvan Farkas
  • Piroska Víg

Abstract

This study highlights the importance of selecting the appropriate Artificial Neural Network (ANN) training algorithm-based accuracy of prediction capacities in photovoltaic (PV) systems. Accurate PV system performance prediction, particularly output voltage and current, is essential for optimising energy generation and ensuring grid stability. This study evaluates the impact of three ANN training algorithms Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) on the prediction of PV voltage and current. The algorithms were tested using solar radiation and temperature as inputs, to determine their effectiveness in handling varying datasets. Therefore, the results indicate that the Levenberg-Marquardt algorithm outperforms the others in terms of speed, memory efficiency, and accuracy, achieving the lowest mean square error (MSE) of 0.0957 and the highest regression value (R= 0.999946). Bayesian Regularization demonstrated strong generalization capabilities with an MSE of 0.1436. The Scaled Conjugate Gradient algorithm performed well but had a slightly higher MSE of 0.1729. besides, Error histograms for all algorithms showed minimal deviations, confirming their predictive accuracy. The findings provide valuable insights for enhancing the accuracy and efficiency of PV system modelling and forecasting, contributing to the advancement of renewable energy integration.

Suggested Citation

  • Aschenaki Altaye & Istvan Farkas & Piroska Víg, 2025. "Impacts of Artificial Neural Network Training Algorithms on the Accuracy of PV System Voltage and Current Predictions," European Journal of Energy Research, European Open Science, vol. 5(3), pages 1-6, May.
    • Handle: RePEc:epw:energy:v:5:y:2025:i:3:id:7161
    DOI: 10.24018/ejenergy.2025.5.3.161
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    References listed on IDEAS

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    1. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    2. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    3. Andreas Fischer & Alexey F. Izmailov & Mikhail V. Solodov, 2024. "The Levenberg–Marquardt method: an overview of modern convergence theories and more," Computational Optimization and Applications, Springer, vol. 89(1), pages 33-67, September.
    4. Markovics, Dávid & Mayer, Martin János, 2022. "Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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