الفهرس 
: LOAD FORECASTING PROBLEM,Only 14 pages are availabe for public view
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Abstract
Data mmmg means extraction of interesting information or
patterns, which is non-trivial, implicit, previously unknown and
potentially useful from data in large databases. Data mining is
used to specify the kind of patterns to be found in data mining
tasks. Data mining tasks can characterize the general properties
of the data and perform inference on the current data in order to
make predictions.
Accurate models for electrical load forecast are essential.
Knowing the load behavior in advance is very important in
planning, analysis and operation of power systems to maintain
reliable, secure and economic electric energy supply.
Forecasting of electricity has always been the essential part of
an efficient power system planning and operation, especially
long term forecasts as it has become increasingly important
since the rise of the competitive energy markets. The aim of
long term load forecast is to predict future electricity demands
based on historical data of some independent parameters such as
total energy sale, total energy generation, GDP etc.
This thesis investigates the suitable model using Data Mining
and Knowledge Discovery in Databases (KDD) to increase the
accuracy and revenue.
AJI the implemented algorithms were written with MATLAB.
The data was collecting from the different sites of the Egyptian
Electricity Sector.
Knowledge discovery process steps are implemented to the time
series data, preprocessing the data in order to detect the missinvalue, odd value, outliers, and normalizing the data. The output
from the preprocessing step is then fed into multiple regression
to predict the coefficient parameters, or neural network for
training. To obtain the forecast, the prediction data of the
independent parameters must be entered into any of the two
techniques (regression, neural network).
These steps (phases) have been carried out for all parameters for
ten models selected for implementations in order to reach the
best model that fulfills good result with the Egyptian Power
System.
The simplest and more accurate model is the model that
contains the following algorithms: natural cluster based
interpolation algorithm for detecting the missing value, the
histogram algorithm for detecting the odd value, and the
regression method for forecasting the long term load forecast.
Also the neural network it seen to be more accurate but more
costly.
The data results showed that the data mining with KDD process
was capable of producing a reasonable forecasting accuracy in
long term load forecast