Forecasting electric energy consumption using neural networks

An artificial neural network model is developed to relate the electric energy consumption in the Eastern Province of Saudi Arabia to the weather data (temperature and humidity), global solar radiation and population. A two layered feedforward neural network is used for the modelling. The inputs to the neural network are the independent variables and the output is the electric energy consumption. Seven years' of data are used for model building and validation. Model adequacy is established by a visual inspection technique and the chi-square test. Model validation, which reflects the suitability of the model for future predictions is performed by comparing the predictions of the model with future data that was not used for model building. Comparison with a regression model shows that the neural network model performs better for predictions.