Self-adaptive radial basis function neural network for short-term electricity price forecasting

Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in an electricity market. A reliable price prediction model based on an advanced self-adaptive radial basis function (RBF) neural network is presented. The proposed RBF neural network model is trained by fuzzy c-means and differential evolution is used to auto-configure the structure of networks and obtain the model parameters. With these techniques, the number of neurons, cluster centres and radii of the hidden layer, and the output weights can be automatically calculated efficiently. Meanwhile, the moving window wavelet de-noising technique is introduced to improve the network performance as well. This learning approach is proven to be effective by applying the RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of the electricity regional reference price from the Queensland electricity market of the Australian National Electricity Market.

[1]  Kit Po Wong,et al.  Differential Evolution, an Alternative Approach to Evolutionary Algorithm , 2006, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[2]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

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

[4]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[5]  Zhao Yang Dong,et al.  Neural Network Models for Electricity Market Forecasting , 2005 .

[6]  N. Amjady.  Day-ahead price forecasting of electricity markets by a new fuzzy neural network , 2006, IEEE Transactions on Power Systems.

[7]  Ludmila I. Kuncheva,et al.  Initializing of an RBF network by a genetic algorithm , 1997, Neurocomputing.

[8]  Yan Xue-feng.  Kinetic Parameter Estimation of Oxidation in Supercritical Water Based on Modified Differential Evolution , 2006 .

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

[10]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  Roderick Murray-Smith,et al.  Multiple Model Approaches to Modelling and Control , 1997 .

[12]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

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

[14]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[15]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[16]  Witold Pedrycz,et al.  Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering , 2006, Neurocomputing.

[17]  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.

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

[19]  C. L. Philip Chen,et al.  A rapid supervised learning neural network for function interpolation and approximation , 1996, IEEE Trans. Neural Networks.

[20]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

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

[22]  Tommy W. S. Chow,et al.  A People-Counting System Using a Hybrid RBF Neural Network , 2004, Neural Processing Letters.

[23]  J. Contreras,et al.  ARIMA models to predict next-day electricity prices , 2002 .

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

[25]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

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

[27]  Chng Eng Siong,et al.  Gradient radial basis function networks for nonlinear and nonstationary time series prediction , 1996, IEEE Trans. Neural Networks.

[28]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[29]  T. Dillon,et al.  Electricity price short-term forecasting using artificial neural networks , 1999 .