Electricity price forecasting with effective feature preprocessing

2006 IEEE Power Engineering Society General Meeting, PES

Volumn , Issue , 2006, Pages

  Zhao, Junhua ^a,b   Dong, Zhaoyang ^a,b   Li, Xue ^a,b  

^a IEEE   (Australia)

^b UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRICITY PRICE; FORECASTING MODELS;

DEREGULATION; ELECTRICITY; MATHEMATICAL MODELS; PROBLEM SOLVING;

ELECTRIC LOAD FORECASTING;

EID: 35348849498     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. J. Contreras, R. Espinola, F.J. Nogales and A.J. Conejo, "ARIMA Models to Predict Next-Day Electricity Prices", IEEE Trans on Power Systems,Vol.18, No.3, pp.1040-1020, Aug 2003.
2. T. Niimura and H.S. Ko. "A day-ahead electricity price prediction based on a fuzzy-neural autoregressive model in a deregulated electricity market". Proc. the 2002 International Joint Conf. on Neural Networks (IJCNN'02), Vol.2, No. 12-17, pp.1362-1366, May 2002.
3. F. J. Nogales, J. Contreras, A. J. Conejo, and R. Espinola, "Forecasting next-day electricity prices by time series models", IEEE Trans. Power System., vol. 17, no. 2, pp. 342-348, May 2002.
4. J.-J. Guo and P.B. Luh, "Selecting Input Factors for Clusters of Gaussian Radial Basis Function Networks to Improve Market Clearing Price Prediction", IEEE Trans on Power Systems, Vol.18, No.2, pp.665-672, May 2003.
5. L. Xu, Z.Y. Dong and A. Tay, Time Series Forecast with Elman Neural Networks and Genetic Algorithms, Book chapter, in Recent Advances in Simulated Evolution and Learning, edt. K.C. Tan, M. H. Lim, X. Yao and L.P. Wang, World Scientific series on Advances in Natural Computation. Sep 2004.
6. B.L. Zhang and Z.Y. Dong, "An Adaptive Neural-wavelet Model for Short-Term Load Forecasting", Electric Power Systems Research, Vol.59, pp.121-129, 2001.
7. H.S. Guirguis, and F.A. Felder, "Further Advances in Forecasting Day-Ahead Electricity Prices Using Time Series Models", KIEE International Transactions on PE, Vol 4-A No. 3 pp. 159-166, 2004.
8. Z. Xu, Z.Y. Dong and W.Q. Liu, "Short-term Electricity Price Forecasting using Wavelet and SVM Techniques", Invited Paper, Proc. of The 3rd International DCDIS Conference on Engineering Applications and Computational Algorithms, May 15-18, 2003, Guelph, Ontario, Canada.
9. Alicia Mateo González, Antonio Muñoz San Roque, and Javier García-González. "Modeling and Forecasting Electricity Prices with Input/Output Hidden Markov Models". IEEE Transactions on Power Systems, Vol. 20, No. 1, FEBRUARY 2005.
10. T. Mount, "Are Price Spikes Predictable, Reproducible and Avoidable?" PSERC seminar presentation, Cornel University, October, 2000.
11. Butler, K.L.; Momoh, J.A. "Detection and classification of line faults on power distribution systems using neural networks". Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on 16-18 Aug. 1993 Page(s):368-371 vol.1.
12. H. Liu, L. Yu. "Toward integrating feature selection algorithms for classification and clustering", IEEE Transaction on Knowledge and Data Engineering, Vol. 17, No. 4, April, 2005.
13. Nealand, J.H.; Bradley, A.B.; Lech, M. "Discriminative feature extraction applied to speaker identification", Signal Processing, 2002 6th International Conference on Volume 1, 26-30 Aug. 2002 Page(s):484-487 vol.1.
14. A.C. Tamhane, D.D. Dunlop. Statistics and Data Analysis: from Elementary to Intermediate. Upper Saddle River, NJ: Prentice Hall, c2000.
15. A.L. Blum and P. Langley, "Selection of Relevant Features and Examples in Machine Learning," Artificial Intelligence, vol. 97, pp. 245-271, 1997.
16. M. Dash, K. Choi, P. Scheuermann, and H. Liu, "Feature Selection for Clustering-a Filter Solution," Proc. Second Int'l Conf. Data Mining, pp. 115-122, 2002.
17. E. Leopold and J. Kindermann, "Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?" Machine Learning, vol. 46, pp. 423-444, 2002.
18. Y. Rui, T.S. Huang, and S. Chang, "Image Retrieval: Current Techniques, Promising Directions and Open Issues," Visual Comm. and Image Representation, vol. 10, no. 4, pp. 39-62, 1999.
19. K.S. Ng and H. Liu, "Customer Retention via Data Mining," AI Rev., vol. 14, no. 6, pp. 569-590, 2000.
20. M.A. Hall, "Correlation-Based Feature Selection for Discrete and Numeric Class Machine Learning," Proc. 17th Int'l Conf. Machine Learning, pp. 359-366, 2000. (CFS)
21. M. Ben-Bassat, "Pattern Recognition and Reduction of Dimensionality," Handbook of Statistics-II, P.R. Krishnaiah and L.N. Kanal, eds., pp. 773-791, North Holland, 1982. (Info)
22. K. Kira and L.A. Rendell, "The Feature Selection Problem: Traditional Methods and a New Algorithm," Proc. 10th Nat'l Conf. Artificial Intelligence, pp. 129-134, 1992. (relief)
23. R. Kohavi and G.H. John, "Wrappers for Feature Subset Selection," Artificial Intelligence, vol. 97, nos. 1-2, pp. 273-324, 1997.
24. W.L. Martinez, A.R. Martinez. Computational Statistics Handbook with MATLAB. Boca Raton: Chapman & Hall/CRC, c2002.
25. Oja, E.; Kiviluoto, K.; Malaroiu, S. "Independent component analysis for financial time series". Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000.
26. H. Zhang, L.Z. Zhang, L. Xie, and J.N. Shen. "The ANN of UMCP forecast based on developed ICA", IEEE International Conference on Electric Utility Deregulation, Reconstruction, and Power Technologies (DRPT2004) April 2004, Hongkong.
27. Petter Skantze, Andrej Gubina, and Marija Ilic. "Bid-based Stochastic Model for Electricity Prices: The Impact of Fundamental Divers on Market Dynamics". Report produced by Energy Laboratory, MIT, [Online] Available: http://lfee.mit.edu/public/el00-004.pdf.
28. J. W. Han, M. Kamber. Data Mining: Concepts and Techniques, San Francisco, Calif.: Morgan Kaufmann Publishers, 2001.
29. N. Lewis. Energy price forecasting: point of view, report produced by Cap Gemini Ernst & Young, Feb, 2003.
30. H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Boston: Kluwer Academic, 1998.
31. I.H. Witten, E. Frank. Data mining: practical machine learning tools and techniques (2nd edition). San Francisco, Calif.: Morgan Kaufman, 2005.
32. J. Yang and V. Honavar, "Feature Subset Selection Using a Genetic Algorithm," Feature Extraction, Construction and Selection: A Data Mining Perspective, pp. 117-136, 1998, second printing, 2001.
33. Garcia, G.N.; Ebrahimi, T.; Vesin, J.-M. "Support vector EEG classification in the Fourier and time-frequency correlation domains". Proceedings of the First International IEEE EMBS Conference on Neural Engineering, 20-22 March 2003 Page(s):591-594.
34. T. Sakai. "Average gain ratio: a simple retrieval performance measure for evaluation with multiple relevance levels". Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, July 2003.
35. V. Vapnik. The Nature of Statistical Learning Theory, Springer Verlag, New York, 1995.
36. Webpage for downloading Australia electricity market price dataset: http://www.nemmco.com.au/data/csv.htm.