In this Work, a feed-forward Artificial Neural Network (ANN) technique using experimental data is designed for predicting the maximum power point of a photovoltaic array. An ANN model training strategy is challenging due to the variations in the training and the operation conditions of a photovoltaic system. In order to improve ANN model accuracy, the Particle Swarm Optimisation (PSO) algorithm is utilized to find the best topology and to calculate the optimum initial weights of the ANN model. Hence, the dilemma between computational time and the best-fitting regression of the ANN model is addressed, as well as the mean squared error being minimized. To evaluate the proposed method, a MATLAB/Simulink model for an installed photovoltaic system is developed. The results show that the optimized feedforward ANN technique based on the PSO algorithm using real data predicts the maximum power point accurately.
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