TITLE:
A Short-Term Electricity Price Forecasting Scheme for Power Market
AUTHORS:
Gao Gao, Kwoklun Lo, Jianfeng Lu, Fulin Fan
KEYWORDS:
Box-Jenkins Method, ARIMA Models, Electricity Markets, Electricity Prices, Forecasting
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.4 No.3D,
October
20,
2016
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 July7th 2010.