الفهرس 
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Abstract
Load forecasting problem is receiving great and growing attention as
being an important and primary tool in power system planning and
operation. Importance of load forecasting becomes more significant in
developing countries with high growth rate such as Egypt. The accuracy of
load forecasting is crucial due to its direct influence on generation
planning, and for its economical impacts. The objective of this thesis is to
perform both long and short term load forecasting based on real historical
data for the Egyptian unified network. To get these forecasts we used one
of the artificial intelligence techniques which is the artificial neural
network. On the other hand, it will be compared to one of the traditional
methods which is the regression model. Performance of both models will
be investigated including effect of weather factors and the results will be
compared to obtain the validation of the proposed techniques. The software
used for designing an operation of ANN is MATLAB 7.5 .The Egyptian
load curve will be carefully analyzed to obtain useful data. Many
experiments will be done by changing neural network parameters and the
results will be observed. Different inputs will be tested using statistical
analysis using SPSS software (Statistical Package for Social Sciences). A
comprehensive discussion will be held about load affecting factors in
Egypt at different time frames. For regression model both univariate and
multi-variate models will be used.