  Due to imprecision and uncertainties in predicting real world problems,
artificial neural network (ANN) techniques have become increasingly useful for
modeling and optimization. This paper presents an artificial neural network
approach for forecasting electric energy consumption. For effective planning
and operation of power systems, optimal forecasting tools are needed for energy
operators to maximize profit and also to provide maximum satisfaction to energy
consumers. Monthly data for electric energy consumed in the Gaza strip was
collected from year 1994 to 2013. Data was trained and the proposed model was
validated using 2-Fold and K-Fold cross validation techniques. The model has
been tested with actual energy consumption data and yields satisfactory
performance.
