|
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.
|