Risk quantification for ANN based short-term load forecasting

Daisuke Iwashita, Hiroyuki Mori

Research output: Contribution to journalArticle

1 Citation (Scopus)


A new risk assessment method for short-term load forecasting is proposed. The proposed method makes use of an artificial neural network (ANN) to forecast one-step-ahead daily maximum loads and evaluate uncertainty of load forecasting. With ANN as the model, the radial basis function (RBF) network is employed to forecast loads due to its good performance. Sufficient realistic pseudo-scenarios are required to carry out quantitative risk analysis. The multivariate normal distribution with the correlation between input variables is used to give more realistic results to ANN. In addition, the method of moment matching is used to improve the accuracy of the multivariate normal distribution. The peak over threshold (POT) approach is used to evaluate risk that exceeds the upper bounds of generation capacity. The proposed method is successfully applied to real data of daily maximum load forecasting.

Original languageEnglish
Pages (from-to)54-62
Number of pages9
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Issue number2
Publication statusPublished - 30 Jan 2009



  • Extreme value theory
  • Moment matching
  • Neural network
  • Risk analysis
  • Short-term load forecasting

Cite this