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The thesis presents two fuzzy logic based neural network to
power system short term load forecasting. Employing the
structure of counter propagation network, the Fuzzy Counter
Propagation Network (FCPN) first clusters the input historical
load data, and accordingly gives different learning rates to
the hidden neurons. By weighting and thus fuzzifying the
activation function of the neurons in the network, the neurons
will possess more capability of classification. The second
approach presented is the Fuzzy Neural Network (FNN). It first
clusters the input load data using unsuper vised learning
scheme. Then based on the results of clustering, parameters of
the Gaussian membership function of each input neuron can be
determined. Finally, the optimal model for the nonlinear input-
output mapping relationship is obtained by the algorithm of
supervised learning. Basically, these two approaches are
intended to ameliorate the problems with training the convent
ional neural networks. Consequently these approaches avoid the
process of trial and errors, as well as the humam involvement
in building the forecasting models, and further enforce the
practicability of the models. To verify the forecasting
accuracy of the proposed approaches, they are both applied to
forecast the one hour ahead load demand of Taiwan Power
(Taipower) system. Results obtained are also compared with
those of Box-Jenkins time series forecasting method as well as
conventional feedforward neural network model. Average relative
forecasting errors of 1.96% ( with standard deviation of
0.0145% ) for the Fuzzy Counter Propagation Network and 1.57% (
with standard deviation 0.1020% ) for the Fuzzy Neural Network
validate our approaches in power system short term load
forecasting.
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