@article {10.3844/ajassp.2009.970.973,
article_type = {journal},
title = {Electricity Load Forecasting based on Framelet Neural Network Technique},
author = {Abd, Mohammed K.},
volume = {6},
year = {2009},
month = {May},
pages = {970-973},
doi = {10.3844/ajassp.2009.970.973},
url = {https://thescipub.com/abstract/ajassp.2009.970.973},
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. },
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}