Short Term Forecasting for Electricity Demand in Egypt

Eman Mohamed, Mohamed A. Ismail, Alyaa R. Zahran

Electricity is important for any nation. It influences not only the economy, but also the political and social aspects of a nation. Forecasting electricity demand is vital for future technical improvements. Short-term electricity demand forecasts are important for controlling of the electric power system. Recently, electricity demand series has found to contain more than one seasonal pattern. Intraday and intraweek seasonal patterns are appeared in the Egyptian electricity demand time series. This study investigates using Artificial Neural Networks in accommodating these seasonality patterns for forecasting hourly electricity demand in Egypt by using seasonal lags as inputs. Different artificial neural networks with different seasonal daily and weekly lags are used. The mean absolute percentage error is used to compare forecasting power of different artificial neural networks. Results indicate the accuracy of forecasts produced by the different artificial neural networks for different time horizons.

Keywords: Artificial neural networks, Double seasonality, Electricity demand forecasting, Mean absolute percentage.