A short-term electricity price forecasting scheme for power market

Gao, Gao and Lo, Kwoklun and Lu, Jianfeng and Fan, Fulin (2016) A short-term electricity price forecasting scheme for power market. World Journal of Engineering and Technology, 4 (3D). pp. 58-65. ISSN 2331-4249

[thumbnail of Gao-etal-WJET2016-Short-term-electricity-price-forecasting-scheme]
Text (Gao-etal-WJET2016-Short-term-electricity-price-forecasting-scheme)
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (320kB)| Preview


    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.

    ORCID iDs

    Gao, Gao ORCID logoORCID: https://orcid.org/0000-0002-2320-9371, Lo, Kwoklun, Lu, Jianfeng ORCID logoORCID: https://orcid.org/0000-0002-7855-1668 and Fan, Fulin ORCID logoORCID: https://orcid.org/0000-0003-2450-6877;