Evolutionary programming versus artificial immune system in evolving neural network for grid-connected photovoltaic system output prediction

  • Authors:

  • Shahril Irwan Sulaiman;Titik Khawa Abdul Rahman;Ismail Musirin;Sulaiman Shaari

  • Affiliations:

  • Faculty of Electrical Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia;Faculty of Electrical Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia;Faculty of Electrical Engineering, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia;Faculty of Applied Sciences, Universiti Teknologi MARA Malaysia, Shah Alam, Selangor, Malaysia

  • Venue:
  • WSEAS Transactions on Systems and Control
  • Year:
  • 2011

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Abstract

This paper presents the evolutionary neural networks for the prediction of energy output from a grid-connected photovoltaic (GCPV) system. Two evolutionary neural network (ENN) models have been proposed using evolutionary programming and artificial immune system (AIS) respectively. The artificial neural network (ANN) employed for these models utilized solar radiation and ambient temperature as its input whereas the kilowatt-hour energy of the GCPV system is the only targeted output. The evolution of ANN involves the search of the optimal number of nodes, the learning rate, the momentum rate, the transfer function and the learning algorithm of a single-hidden layer multi-layer feedforward ANN. The results showed that evolutionary programming-ANN (EPANN) outperformed artificial immune system-ANN (AISANN) in terms of correlation coefficient, R as well as computation time. In addition, EPANN had also produced better convergence of the evolving parameters compared to the AISANN.