Classification of Future Electricity Market Prices

Forecasting short-term electricity market prices has been the focus of several studies in recent years. Although various approaches have been examined, achieving sufficiently low forecasting errors has not been always possible. However, certain applications, such as demand-side management, do not require exact values for future prices but utilize specific price thresholds as the basis for making short-term scheduling decisions. In this paper, classification of future electricity market prices with respect to pre-specified price thresholds is introduced. Two alternative models based on support vector machines are proposed in a multi-class, multi-step-ahead price classification context. Numerical results are provided for classifying prices in Ontario's and Alberta's markets.

[1]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[2]  Ashwani Kumar,et al.  Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market , 2010 .

[3]  Dominique Y. Dupont,et al.  When Supply Meets Demand: The Case of Hourly Spot Electricity Prices , 2008, IEEE Transactions on Power Systems.

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

[5]  C. Rodriguez,et al.  Energy price forecasting in the Ontario competitive power system market , 2004, IEEE Transactions on Power Systems.

[6]  Kurt Hornik,et al.  The support vector machine under test , 2003, Neurocomputing.

[7]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[8]  Junhua Zhao,et al.  A Framework for Electricity Price Spike Analysis With Advanced Data Mining Methods , 2007, IEEE Transactions on Power Systems.

[9]  Shu Fan,et al.  Next-day electricity-price forecasting using a hybrid network , 2007 .

[10]  G. Baudat,et al.  Feature vector selection and projection using kernels , 2003, Neurocomputing.

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

[12]  K. Bhattacharya,et al.  Economic Impact of Electricity Market Price Forecasting Errors: A Demand-Side Analysis , 2010, IEEE Transactions on Power Systems.

[13]  Fernando Olsina,et al.  Short-term optimal wind power generation capacity in liberalized electricity markets , 2007 .

[14]  Chen-Ching Liu,et al.  Day-Ahead Electricity Price Forecasting in a Grid Environment , 2007, IEEE Transactions on Power Systems.

[15]  E. Bompard,et al.  Dynamic price forecast in a competitive electricity market , 2007 .

[16]  K. Bhattacharya,et al.  The Operation of Ontario's Competitive Electricity Market: Overview, Experiences, and Lessons , 2007, IEEE Transactions on Power Systems.

[17]  C. Cañizares,et al.  Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices , 2006, IEEE Transactions on Power Systems.

[18]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[19]  J. Ramos,et al.  Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques , 2007, IEEE Transactions on Power Systems.

[20]  P.B. Luh,et al.  Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method , 2005, IEEE Transactions on Power Systems.

[21]  T. Senjyu,et al.  A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method , 2007, IEEE Transactions on Power Systems.

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

[23]  N. Amjady,et al.  Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm , 2009, IEEE Transactions on Power Systems.

[24]  Ashwani Kumar,et al.  Day-ahead Price Forecasting in Ontario Electricity Market Using Variable-segmented Support Vector Machine-based Model , 2009 .

[25]  F. Nogales,et al.  Price-taker bidding strategy under price uncertainty , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[26]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[27]  Jose Alvarez-Ramirez,et al.  Time-dependent correlations in electricity markets , 2010 .

[28]  J. Contreras,et al.  Forecasting next-day electricity prices by time series models , 2002 .

[29]  A.M. Gonzalez,et al.  Modeling and forecasting electricity prices with input/output hidden Markov models , 2005, IEEE Transactions on Power Systems.

[30]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[31]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[32]  Jay Apt,et al.  An economic welfare analysis of demand response in the PJM electricity market , 2008 .

[33]  R. Weron,et al.  Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models , 2008 .

[34]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[35]  Z. Dong,et al.  A Statistical Approach for Interval Forecasting of the Electricity Price , 2008, IEEE Transactions on Power Systems.

[36]  K. Tomsovic,et al.  Discovering Price-Load Relationships in California's Electricity Market , 2001, IEEE Power Engineering Review.