Load forecasting plays an essential role both in developed and developing
countries for policymakers and related organizations. It helps an electrical utility
to make important decisions including decisions on purchasing and generating
electrical power, load switching, and infrastructure development. In recent years
Artificial Neural Networks (ANNs) have been applied for short-term power load
forecasting (STPLF). This work presents a study of STPLF for the Iraqi national
grid by means of Radial Basis Function NN(RBFNN) and Multi-Layer
Perceptron NN (MLPNN) model. Inputs to the ANN are past loads and the output
of the ANN is the load forecast for given days. Historical load data obtained from
the Control and Operation Office at the Iraqi ministry of electricity has been split
into two main parts, where 50% of the data are used for the training and the other
50% has been devoted to test the trained network. Simulations have been
accomplished in MATLAB environment, where the data have been preprocessed
and rearranged. Lastly, the simulation results proved that the predicted load
values are following closely the actual load.