Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption
This provides a tool for energy operators in power systems to improve planning and profit, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of forecasting electric energy consumption using artificial neural networks, achieving satisfactory performance on monthly data from the Gaza Strip from 1994 to 2013.
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.