You are in:Home/Publications/W.I. Mansour, M. Moenes Salama, H. M. Mahmoud, A. T. Ghareeb, “Long Term Load Forecasting for the Egyptian Network using ANN and Regression Models”, The 21st International Conference and Exhibition on Electricity Distribution ( CIRED 2011), 6-9 June 2011, Frankfurt, Germany, Paper No. 0043.

Prof. Mohamed Moenes Mohamed Salama :: Publications:

Title:
W.I. Mansour, M. Moenes Salama, H. M. Mahmoud, A. T. Ghareeb, “Long Term Load Forecasting for the Egyptian Network using ANN and Regression Models”, The 21st International Conference and Exhibition on Electricity Distribution ( CIRED 2011), 6-9 June 2011, Frankfurt, Germany, Paper No. 0043.
Authors: W.I. Mansour, M. Moenes Salama, H. M. Mahmoud, A. T. Ghareeb
Year: 2011
Keywords: Not Available
Journal: The 21st International Conference and Exhibition on Electricity Distribution ( CIRED 2011), Frankfurt, Germany
Volume: Paper No. 0043.
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper Mohamed Moenes Mohamed Salama _CIRED2011_0043 Long Term Load Forecasting for the Egyptian Network.pdf
Supplementary materials Not Available
Abstract:

ABSTRACT The major concern for every electrical utility is the ability to provide reliable and uninterrupted service to their customers. The challenge becomes more significant with the fast and sharp increasing need for electric energy in the fast developing countries such as Egypt. Load forecasting is mandatory for planning, operation and control of power system. This paper concerns with long term load forecasting and presents a comparison between twomodels when applied to the Egyptian unified network, thesemodels are Artificial Neural Network (ANN) model and regression model .Data preprocessing techniques have been applied To improve forecasting accuracy of the model. Forecasting capability of each approach is evaluated by calculating two separate statistical evaluations of the Mean Absolute Percentage Error (MAPE) and the Average Absolute Percentage Error (AAVE).

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