Abstract
In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.
Highlights
The global reform of power industry transferred electricity producers and purchasers from not be able to select their suppliers to full free choice in the last decades
This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market
A sliding training window consisting of the historic price data in the most recent 8 weeks is used to determine the parameters of the ARIMA and ANN models from which the price predictions for one step, two steps and three steps (1.5 hours) ahead are estimated respectively
Summary
The global reform of power industry transferred electricity producers and purchasers from not be able to select their suppliers to full free choice in the last decades. Price forecasting is becoming increasingly relevant to all participants in the new competitive electric power markets [1]. If electricity price can be accurately predicted, for power producers, power generation companies could develop suitable generation plan and maximize corporate profits by grasping market dynamics. It could improve monitoring ability for market operation and solve problems in the market based on the forecast results of grid. A large number of forecasting models and methods have been tried. These methods can be divided into two categories: classical approaches such as auto regressive integrated moving average (ARIMA) models and artificial intelligence (AI) based techniques [3]. The New Electricity Trading Arrangements (NETA) has put into use since 27 March 2010 [4]
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