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

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.