A general method for electricity market price spike analysis

Many techniques have been employed to forecast electricity market prices, and have achieved good results. These techniques mostly focus on normal price forecasting, not on the forecasting of price spikes. Data mining techniques have been successfully applied to forecast the value of price spikes under the condition that a spike appears. However, an effective method of predicting the occurrence of the spikes is yet to be seen. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with spike value prediction techniques, the proposed approach can give a comprehensive price spike forecasting. In this paper, data pre-process techniques are described to find the attributes relevant to the spikes. Then a simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen and discussed in detail. Actual market data are used to test the proposed model, and promising results have been obtained.

[1]  H. Guirguis,et al.  Further Advances in Forecasting Day-Ahead Electricity Prices Using Time Series Models , 2004 .

[2]  T. Mount,et al.  On the first price spike in summer , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[3]  P. Luh,et al.  Selecting input factors for clusters of Gaussian radial basis function networks to improve market clearing price prediction , 2003 .

[4]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[5]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[6]  T. Niimura,et al.  A day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[7]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[8]  Zhao Yang Dong,et al.  An adaptive neural-wavelet model for short term load forecasting , 2001 .

[9]  D. L. Pepyne,et al.  Gaming and Price Spikes in Electric Power Markets , 2001, IEEE Power Engineering Review.

[10]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[11]  S. Borenstein,et al.  Electricity Restructuring: Deregulation or Reregulation? , 2000 .

[12]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[13]  Severin Borenstein,et al.  Understanding Competitive Pricing and Market Power in Wholesale Electricity Markets , 1999 .

[14]  Thomas J. Overbye,et al.  Analysis and visualization of market power in electric power systems , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[15]  Stephen J. Rassenti,et al.  Market design and motivated human trading behavior in electricity markets , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[16]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[17]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[18]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[20]  N. Littlestone.  Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[21]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[22]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[23]  Z. Dong,et al.  Electricity market price spike forecast with data mining techniques , 2005 .

[24]  Zhao Yang Dong,et al.  Short-term electricity price forecasting using wavelet and SVM techniques , 2003 .

[25]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[26]  D. Sansom,et al.  Neural networks for forecasting electricity pool price in a deregulated electricity supply industry , 1999 .