Forecasting the electricity demand has been the key task to reach the long-term goals of
European Union. Inadequacy of accurate measurement of electricity demand which further
causes the under generation or over generation of electricity and may also results in huge
investments on energy resources. Predictive analysis and time series forecasting methods are
used to overcome these difficulties. The aim of this paper is to short-term forecast the electricity
demand using the seasonal auto-regressive Integrated moving average (SARIMA) and compare
the results with the Multi-layer perceptron (MLP) model. The half-hourly dataset of London
city is used for analyzing the electricity demand which is collected from UK power networks
along with weather and holidays in the same city from November 2011 to February 2014. The
forecasting plots has made based on the maximum, minimum and average consumption and
then compare the mean absolute error (MAE), mean absolute percentage error (MAPE), mean
square error (MSE) and root mean square error (RMSE) of SARIMA with the MLP model.
During the evaluation MLP outperformed the SARIMA model and the prediction graphs are
displayed using User Interface.