Abstract: The accurate prediction of annual electricity consumption is crucial in managing energy operations. The neural network (NN) has achieved a lot of achievements in yearly electricity consumption prediction due to its universal approximation property. However, the well-known back-propagation (BP) algorithms for training NN has easily got stuck in local optima. In this paper, we study the weights initialization of NN for the prediction of annual electricity consumption using the Cultural algorithm (CA), and the proposed algorithm is named as NN-CA. The NN-CA was compared to the weights initialization using the other six metaheuristic algorithms as well as the BP. The experiments were conducted on the annual electricity consumption datasets taken from 21 countries. The experimental results showed that the proposed NN-CA achieved more productive and better prediction accuracy than other competitors. This result indicates the possible consequences of the proposed NN-CA in the application of annual electricity consumption prediction.

Gawalee Phatai, Sirapat Chiewchanwattana and Khamron Sunat, “Cultural Algorithm Initializes Weights of Neural Network Model for Annual Electricity Consumption Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110613

@article{Phatai2020,
title = {Cultural Algorithm Initializes Weights of Neural Network Model for Annual Electricity Consumption Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110613},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110613},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Gawalee Phatai and Sirapat Chiewchanwattana and Khamron Sunat}
}