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has been cited by the following article:

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

  AUTHORS: Gao Gao, Kwoklun Lo, Fulin Fan

  KEYWORDS: Electricity Markets, Electricity Prices, ARIMA Models, ANN Models, Short-Term Forecasting

  JOURNAL NAME: Energy and Power Engineering, Vol.9 No.4B, April 6, 2017

  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.