Risk assessment due to local demand forecast uncertainty in the competitive supply industry

K.L. Lo, Y. Wu

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

A risk assessment on local demand forecast uncertainty is presented. The aim is to highlight high-risk periods over different lengths of time and daily value-at-risk (VAR) due to load forecast errors. A number of load forecasts have been performed, and the load forecast is based on ARIMA models and ANN structures. With the residuals from load forecasting, the risk indexes over different time periods and seasons are formed. Moreover, a new methodology using the standard deviation of load increment on evaluating the risk is proposed. In contrast with the standard forecasting method that relies on a sophisticated forecast procedure, the new approach provides a useful and fast method to evaluate the risk due to load forecast uncertainty for a variety of local demand profiles. Finally, the VAR methodology combined with the NETA system is applied to a local electricity supplier in the UK.
LanguageEnglish
Pages573-582
Number of pages9
JournalIEE Proceedings Generation Transmission and Distribution
Volume150
Issue number5
DOIs
Publication statusPublished - Sep 2003

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Risk assessment
Industry
Uncertainty
Electricity

Keywords

  • load forecasting
  • neural nets
  • power system analysis computing
  • risk management

Cite this

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Risk assessment due to local demand forecast uncertainty in the competitive supply industry. / Lo, K.L.; Wu, Y.

In: IEE Proceedings Generation Transmission and Distribution, Vol. 150, No. 5, 09.2003, p. 573-582.

Research output: Contribution to journalArticle

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AU - Wu, Y.

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AB - A risk assessment on local demand forecast uncertainty is presented. The aim is to highlight high-risk periods over different lengths of time and daily value-at-risk (VAR) due to load forecast errors. A number of load forecasts have been performed, and the load forecast is based on ARIMA models and ANN structures. With the residuals from load forecasting, the risk indexes over different time periods and seasons are formed. Moreover, a new methodology using the standard deviation of load increment on evaluating the risk is proposed. In contrast with the standard forecasting method that relies on a sophisticated forecast procedure, the new approach provides a useful and fast method to evaluate the risk due to load forecast uncertainty for a variety of local demand profiles. Finally, the VAR methodology combined with the NETA system is applied to a local electricity supplier in the UK.

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KW - neural nets

KW - power system analysis computing

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