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 (https://doi.org/10.4236/wjet.2016.43D008)
Preview |
Text.
Filename: Gao_etal_WJET2016_Short_term_electricity_price_forecasting_scheme.pdf
Final Published Version License: Download (320kB)| Preview |
Abstract
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: https://orcid.org/0000-0002-2320-9371, Lo, Kwoklun, Lu, Jianfeng ORCID: https://orcid.org/0000-0002-7855-1668 and Fan, Fulin ORCID: https://orcid.org/0000-0003-2450-6877;-
-
Item type: Article ID code: 58233 Dates: DateEvent31 October 2016Published20 October 2016Published Online13 October 2016AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering
Social Sciences > CommerceDepartment: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 25 Oct 2016 09:26 Last modified: 12 Dec 2024 04:49 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/58233