Abstract
This paper presents a hybrid Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) for predicting the kWh output from a grid-connected photovoltaic (GCPV) system. In this study, the ANN-based prediction utilized solar irradiance (SI), ambient temperature (AT) and module temperature (MT) as the inputs and kWh energy from the GCPV system as the sole output. Besides that, Particle Swarm Optimization (PSO) was used to optimize the number of neurons in the hidden layer during the ANN training process such that the Root Mean Square Error (RMSE) of the prediction was minimized. After the training process, testing was performed to validate the ANN training. The results showed that the proposed hybrid PSO-ANN had outperformed the hybrid Fast Evolutionary Programming-Artificial Neural Network (FEP-ANN) in producing lower RMSE. In addition, the optimal learning algorithm and population size in PSO were also investigated in this study.
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