A short-term electricity price forecasting scheme for power market

Gao Gao, Kwoklun Lo, Jianfeng Lu, Fulin Fan

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Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July 7th 2010.
Original languageEnglish
Pages (from-to)58-65
Number of pages8
JournalWorld Journal of Engineering and Technology
Issue number3D
Early online date20 Oct 2016
Publication statusPublished - 31 Oct 2016
Event6th World Congress on Engineering and Technology: 6th Power Engineering and Automation Conference - Shanghai, China
Duration: 21 Oct 201623 Oct 2016


  • Box-Jenkins method
  • ARIMA models
  • electricity market
  • electricity price
  • forecasting
  • autoregressive integrated moving average
  • best-fitted time-domain model
  • historical price data


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