A clustering-based fuzzy wavelet neural network model for short-term load forecasting.
Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.
School of Electronics and Computer Science, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UK. V.Kodogiannis@westminster.ac.uk
Vassilis S
VS
Kodogiannis
Mahdi
M
Amina
Ilias
I
Petrounias
D000465:Algorithms; D017143:Fuzzy Logic; D016571:Neural Networks, Computer