A collaborative fuzzy-neural approach for long-term load forecasting in Taiwan

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Forecasting the long-term load in a country is a critical task for the government. In addition, establishing a precise upper bound for the long-term load avoids unnecessary power plant investment. For these purposes, a collaborative fuzzy-neural approach is proposed in this study. In the proposed approach, multiple experts construct their own fuzzy back propagation networks from various viewpoints to forecast the long-term load in a country. To aggregate these long term load forecasts, fuzzy intersection is applied. After that, a radial basis function network is constructed to defuzzify the aggregation result and to generate a representative/crisp value. The practical case of Taiwan is used to evaluate the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology improved both the precision and accuracy of long term load forecasting by 40% and 99%, respectively. In addition, the proposed methodology made it possible to accurately forecast the average and peak values of the annual energy consumption at the same time.

Original languageEnglish
Pages (from-to)663-670
Number of pages8
JournalComputers and Industrial Engineering
Issue number3
StatePublished - 1 Nov 2012


  • Annual energy consumption
  • Collaborative
  • Fuzzy back propagation network
  • Load forecasting
  • Radial basis function network

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