A Short-Term Electricity Price Forecasting Scheme for Power Market

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 1 st to July7 th 2010.

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

“…In the meanwhile, the demand side could consume the electricity energy within a particular time slot when lower electricity price is forecasted. It therefore could reduce the total cost and improve the market competitiveness [5].…”

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

“…For regulators, it could improve monitoring ability for market operation and solve problems in the market based on the forecast results of grid. Regulators also can formulate relevant strategies and lead right development of power market through the trend of electricity price changes [2].…”