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Articles
  • OpenAccess
  • A Short-Term Electricity Price Forecasting Scheme for Power Market  [CET 2016]
  • DOI: 10.4236/wjet.2016.43D008   PP.58 - 65
  • Author(s)
  • Gao Gao, Kwoklun Lo, Jianfeng Lu, Fulin Fan
  • 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.
  • KEYWORDS
  • Box-Jenkins Method, ARIMA Models, Electricity Markets, Electricity Prices, Forecasting
  • References
  • [1]
    von der Fehr, N.-H.M. and Harbord, D. (1993) Spot Market Competition in the UK Electricity Industry. The Economic Journal, 103, 531-546.
    [2]
    Nicolaisen, J. and Petro, V. (2001) Tesfatsion. Market Power and Efficiency in a Computational Electricity Market with Dis-criminatory Double-Auction Pricing. IEEE Transactions on Evolutionary Computation, 5.
    [3]
    Saini, A. and Saxena, A.K. (2010) Optimal Power Flow Based Congestion Management Methods for Competitive Electricity Markets. International Journal of Computer and Electrical Engineering, 2, 1793-8163.
    http://dx.doi.org/10.7763/ijcee.2010.v2.116
    [4]
    Li, G. (2007) Day-Ahead Electricity Price Forecasting in a Grid Environment. IEEE Transactions on Power Systems, 22.
    [5]
    Giuliettia, M., Grossib, L. and Waterson, M. (2010) Price Transmission in the UK Electricity Market: Was NETA Beneficial? Energy Economics, 32, 1165-1174.
    http://dx.doi.org/10.1016/j.eneco.2010.01.008
    [6]
    Lo, K.L. and Wu, Y.K. (2003) Risk Assessment Due to Local Demand Forecast Uncer-tainty in the Competitive Supply Industry. IEE Proc.-Gener. Transm. Distrib., 150.
    http://dx.doi.org/10.1049/ip-gtd:20030641
    [7]
    Naylor, T.H., Seaks, T.G. and Wichern, D.W. (1972) Box-Jenkins Methods: An Alternative to Econometric Models. International Statistical Review/Revue Internationale de Statistique, 40, 123-137.
    [8]
    Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2008) Time Series Analysis: Forecasting and Control. 4th Edition, Wiley, Oxford.
    http://dx.doi.org/10.1002/9781118619193
    [9]
    Nogales, F.J., Contreras, J., Conejo, A.J. and Espínola, R. (2002) Forecasting Next-Day Electricity Prices by Time Series Models. IEEE Trans. Power Syst., 17, 342-348.
    http://dx.doi.org/10.1109/TPWRS.2002.1007902
    [10]
    Hoa, S.L., Xieb, M. and Gohb, T.N. (2002) A Comparative Study of Neural Network and Box-Jenkins ARIMA Modeling in Time Series Prediction. Computers & Industrial Engineering, 42, 371-375.
    http://dx.doi.org/10.1016/S0360-8352(02)00036-0
    [11]
    Aggarwal, S.K., Saini, L.M. and Kumar, A. (2009) International Journal of Electrical Power & Energy Systems. Electrical Power and Energy Systems, 31, 13-22.
    http://dx.doi.org/10.1016/j.ijepes.2008.09.003
    [12]
    Tran, N. and Reed, D.A. (2004) Automatic ARIMA Time Series Modeling for Adaptive I/O Prefetching. IEEE Transactions on Parallel and Distributed Systems, 15.
    [13]
    Devi, B.U., Sundar, D. and Alli, P. (2013) An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50. International Journal of Data Mining & Knowledge Management Process, 65-78.
    http://dx.doi.org/10.5121/ijdkp.2013.3106
    [14]
    Chai, T. and Draxler, R.R. (2014) Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?—Arguments against Avoiding RMSE in the Literature. Copernicus Publications on Behalf of the European Geosciences Union.
    [15]
    Fan, F., Bell, K. and Infield, D. (2016) Probabilistic Real-Time Thermal Rating Forecasting for Overhead Lines by Conditionally Heteroscedastic Auto-Regressive Models. IEEE Trans- actions on Power Delivery.
    http://dx.doi.org/10.1109/TPWRD.2016.2577140
    [16]
    Fan, F., Bell, K. and Infield, D. (2016) Probabilistic Weather Forecasting for Dynamic Line Rating Studies. Proc. IEEE PowerTech Conference, 1-6.
    http://dx.doi.org/10.1109/pscc.2016.7540854

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