A clustering-based fuzzy wavelet neural network model for short-term load forecasting

Vassilis S. Kodogiannis, Mahdi Amina, Ilias Petrounias

Research output: Contribution to journalArticlepeer-review


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. © 2013 World Scientific Publishing Company.
Original languageEnglish
Article number1350024
JournalInternational Journal of Neural Systems
Issue number5
Publication statusPublished - Oct 2013


  • expectation-maximization algorithm
  • fuzzy clustering
  • fuzzy wavelet neural networks
  • neural networks
  • Short-term load forecasting


Dive into the research topics of 'A clustering-based fuzzy wavelet neural network model for short-term load forecasting'. Together they form a unique fingerprint.

Cite this