Electricity reference price forecasting with Fuzzy C-means and Immune Algorithm

A new hybrid training method for radial basis function (RBF) neural network is presented in this paper. The proposed methodology produces RBF neural network models based on specially designed fuzzy C-means (FCM) and fuzzy immune algorithm (FIA), which are used to auto-configure the structure of networks and obtain the model parameters. With the proposed method, the number of hidden layer neurons and cluster centers are automatically determined according to the given data; both the output weight values and cluster radii are calculated by fuzzy immune algorithm. Meanwhile, the wavelet de-noising technique is introduced to ensure the neural network performance. This learning approach is proved to be effective by applying the optimized RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of Queensland electricity reference price from Australian National Electricity Market.

[1]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[2]  E. Michael Azoff,et al.  Neural Network Time Series: Forecasting of Financial Markets , 1994 .

[3]  George G. Karady,et al.  Effect of probabilistic inputs on neural network-based electric load forecasting , 1996, IEEE Trans. Neural Networks.

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

[5]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

[6]  Miguel Felder,et al.  Verification of real-time designs: combining scheduling theory with automatic formal verification , 1999, ESEC/FSE-7.

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

[8]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[9]  Mats Per Erik Heimdahl,et al.  Specification-based prototyping for embedded systems , 1999, ESEC/FSE-7.

[10]  M. L. Kothari,et al.  Orthogonal least squares learning algorithm based radial basis function (RBF) network adaptive power system stabilizer , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[11]  Gwo-Ching Liao,et al.  Application embedded chaos search immune genetic algorithm for short-term unit commitment , 2004 .

[12]  L. G. van Willigenburg,et al.  Efficient Differential Evolution algorithms for multimodal optimal control problems , 2003, Appl. Soft Comput..

[13]  B. Ramsay,et al.  Prediction of system marginal price in the UK Power Pool using neural networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[14]  Hugo Fierz,et al.  The CIP method: component- and model-based construction of embedded systems , 1999, ESEC/FSE-7.

[15]  A. Papalexopoulos,et al.  Forecasting power market clearing price and quantity using a neural network method , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

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

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

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

[19]  D.W. Bunn,et al.  Forecasting loads and prices in competitive power markets , 2000, Proceedings of the IEEE.

[20]  Hongbo Shi,et al.  Soft Sensor Modeling for Temperature Measurement of Texaco Gasifier Based on an Improved RBF Neural Network , 2006, 2006 IEEE International Conference on Information Acquisition.

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

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

[23]  Pablo Moscato,et al.  Memetic algorithms: a short introduction , 1999 .

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

[25]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .