Staff Members - Benha University
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.
The 21st International Conference and Exhibition on Electricity Distribution ( CIRED 2011), Frankfurt, Germany • 2011
Publication Information
Authors
W.I. Mansour, M. Moenes Salama, H. M. Mahmoud, A. T. Ghareeb
Keywords
Not Available
Journal
The 21st International Conference and Exhibition on Electricity Distribution ( CIRED 2011), Frankfurt, Germany
Publisher
Not Available
Volume
Paper No. 0043.
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Not Available
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).
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).