ANN-based risk assessment for short-term load forecasting

H. Mori, D. Iwashita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

A new risk assessment method is proposed for short-term load forecasting. The proposed method makes use of the RBFN (Radial basis function network) to forecast loads due to the good performance. Sufficient realistic pseudo-scenarios are required to carry out quantitative risk analysis in the Monte-Carlo simulation. The multivariate normal distribution with the correlation between the input variables of RBFN is used to give more realistic results. In addition, this paper employs the moment matching method to improve the accuracy of the multivariate normal distribution. The peak over threshold (POT) approach is used to evaluate the risk that exceeds the upper bound of generation capacity. The proposed method is successfully applied to real data of one-step ahead daily maximum load forecasting.

Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05
Pages446-451
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2005
Event13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05 - Arlington, VA, United States
Duration: 6 Nov 200510 Nov 2005

Publication series

NameProceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05
Volume2005

Conference

Conference13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05
CountryUnited States
CityArlington, VA
Period6/11/0510/11/05

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Keywords

  • Artificial neural network
  • Extreme value theory
  • Moment matching
  • Radial basis function network
  • Risk analysis
  • Short-term load forecasting

Cite this

Mori, H., & Iwashita, D. (2005). ANN-based risk assessment for short-term load forecasting. In Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05 (pp. 446-451). [1599305] (Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05; Vol. 2005). https://doi.org/10.1109/ISAP.2005.1599305