American Journal of Applied Sciences

Electricity Load Forecasting based on Framelet Neural Network Technique

Mohammed K. Abd

DOI : 10.3844/ajassp.2009.970.973

American Journal of Applied Sciences

Volume 6, Issue 5

Pages 970-973

Abstract

Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This study shows Electricity Load Forecasting modeling based on Framelet Neural Network Technique (FNN) for Baghdad City. Framelet technique is implemented to the time series data, decomposing the data into number of Framelet coefficient signals. The decomposed signals are then fed into neural network for training. To obtain the predict forecast, the outputs from the neural network are recombined using the same Framelet technique. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in short term load forecast.

Copyright

© 2009 Mohammed K. Abd. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.