Long-term load forecasting by a collaborative fuzzy-neural approach

Tin-Chih Chen*, Yu Cheng Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

47 Scopus citations


Long-term power load forecasting is of major importance for power suppliers to define the future power consumption of a given region. However, it is not easy to contend with the uncertainty of the long-term load. In order to effectively forecast the long-term load, a collaborative principal component analysis and fuzzy feed-forward neural network (PCA-FFNN) approach is proposed in this study. The difference between this and existing methods is that the collaborative PCA-FFNN approach takes into account the different points of view in a more efficient way, and therefore the results obtained are more comprehensive and more in-depth. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own PCA-FFNNs to forecast the long-term load based on their views. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, the partial-consensus fuzzy intersection and radial basis function network (PCFI-RBF) approach is used. The effectiveness of the proposed methodology is illustrated with a case study.

Original languageEnglish
Pages (from-to)454-464
Number of pages11
JournalInternational Journal of Electrical Power and Energy Systems
Issue number1
StatePublished - 1 Dec 2012


  • Collaborative intelligence
  • Fuzzy neural network
  • Load forecasting
  • Long-term
  • Principal component analysis

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