Risk assessment due to electricity price forecast uncertainty in UK electricity market

Gao Gao, Kwoklun Lo, Jianfeng Lu

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

1 Citation (Scopus)
12 Downloads (Pure)

Abstract

This paper illustrates the risk assessment on electricity price forecast uncertainty. The high-risk periods under different time have been indicated. Autoregressive integrated moving average (ARIMA) models and artificial neural network (ANN) techniques are introduced to forecast electricity prices in UK electricity market. Also, this paper investigates the risk index of electricity prices due to forecast uncertainties in the competitive power market through two aspects – daily and seasonal. This risk index is calculated using the errors of short-term electricity price forecast. The input data of forecasting models is divided into weekday and weekend profiles and this is done to observe the different electricity price dynamic risks between weekdays and weekends.
Original languageEnglish
Title of host publication2017 52nd International Universities Power Engineering Conference (UPEC)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
DOIs
Publication statusPublished - 21 Dec 2017
Event52nd International Universities' Power Engineering Conference - T.E.I. of Crete, Heraklion, Crete, Greece
Duration: 29 Aug 20171 Sep 2017
http://www.upec2017.com/

Conference

Conference52nd International Universities' Power Engineering Conference
Abbreviated titleUPEC 2017
CountryGreece
CityHeraklion, Crete
Period29/08/171/09/17
Internet address

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Keywords

  • electricity market
  • electricity price forecasting
  • risk assessment
  • risk index

Cite this

Gao, G., Lo, K., & Lu, J. (2017). Risk assessment due to electricity price forecast uncertainty in UK electricity market. In 2017 52nd International Universities Power Engineering Conference (UPEC) Piscataway, NJ: IEEE. https://doi.org/10.1109/UPEC.2017.8231903