Bacterial foraging optimization based neural network for short-term load forecasting

Yudong Zhang*, Lenan Wu, Shuihua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Because of the nonlinear characteristics of the power system loads, the short-term load forecasting is difficult to realize. The neural network (NN) was employed in this study for forecasting. However, NN is easy to be trapped in local minima and converge too slow. The traditional training methods based on gradient searching technique are not effective and fast. Therefore, bacterial foraging optimization (BFO) was adopted to train the NN. BFO is a novel and powerful global search technique, and it can find the weights/biases of the neural network quickly and accurately. Experiments indicate that the proposed BFO-NN is superior to GA-NN with respect to convergence speed and forecast accuracy.

Original languageEnglish
Pages (from-to)2099-2105
Number of pages7
JournalJournal of Computational Information Systems
Issue number7
Publication statusPublished - Jul 2010
Externally publishedYes


  • Bacterial foraging optimization
  • Forward neural network
  • Short-term load forecasting


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