Abstract
All over the world, energy is vitally important because it affects life-standard economy, and social growth. Electricity is one of the main significant forms of energy which is usually needed to be generated and cannot be stored physically. It was the main goal to generate the required electricity to meet a future need in previous studies. To maintain the level of electricity needed constantly, a good system needs to be designed for avoiding waste or shortage. This paper proposes an alternative topology of neural network which is known as Elman network. In the case of Elman networks (ENs), majority techniques only identify topologies in which neurons are connected to each other from hidden to input layer. However, training algorithm of Elman network has a number of disadvantages, like network stagnation and getting stuck in a minimum and low local speed of convergence. This study suggests Cuckoo search algorithm (CS) to enhance training time of EN for high precision and fast convergence. Performance of Cuckoo search Elman (CSElman) is compared to artificial bee colony (ABC) and with similar hybrid techniques. Simulation results are displayed that prove the proposed CSElman is better than the other algorithms in this research with respect to accuracy and the speed of convergence.
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Acknowledgements
The researchers would like to say special thanks to “University of Agriculture Peshawar Pakistan for supporting this project.”
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Khan, A. et al. (2019). Forecasting OPEC Electricity Generation Based on Elman Network Trained by Cuckoo Search Algorithm. In: Herawan, T., Chiroma, H., Abawajy, J. (eds) Advances on Computational Intelligence in Energy. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-69889-2_4
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DOI: https://doi.org/10.1007/978-3-319-69889-2_4
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