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Load Forecasting with Support Vector Machines and Semi-parametric Method

  • J. A. Jordaan
  • A. Ukil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

Abstract

A new approach to short-term electrical load forecasting is investigated in this paper. As electrical load data are highly non-linear in nature, in the proposed approach, we first separate out the linear and the non-linear parts, and then forecast using the non-linear part only. Semi-parametric spectral estimation method is used to decompose a load data signal into a harmonic linear signal model and a non-linear trend. A support vector machine is then used to predict the non-linear trend. The final predicted signal is then found by adding the support vector machine predicted trend and the linear signal part. The performance of the proposed method seems to be more robust than using only the raw load data. This is due to the fact that the proposed method is intended to be more focused on the non-linear part rather than a diluted mixture of the linear and the non-linear parts as done usually.

Keywords

Support Vector Machine Load Data Load Forecast Support Vector Machine Training Auto Regressive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Papadakis, S.E., Theocharis, J.B., Kiartzis, S.J., Bakirtzis, A.G.: A novel approach to short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems 13, 480–492 (1998)CrossRefGoogle Scholar
  2. 2.
    Piras, A., Germond, A., Buchenel, B., Imhof, K., Jaccard, Y.: Heterogeneous artificial neural network for short term electrical load forecasting. IEEE Transactions on Power Systems 11, 397–402 (1996)CrossRefGoogle Scholar
  3. 3.
    Bitzer, B., Rösser, F.: Intelligent Load Forecasting for Electrical Power System on Crete. In: UPEC 1997 Universities Power Engineering Conference, UMIST-University of Manchester (1997)Google Scholar
  4. 4.
    Pelckmans, K., Suykens, J., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., Vandewalle, J.: LS-SVMlab Toolbox User’s Guide, Version 1.5. Catholic University Leuven, Belgium (2003), [Online] Available from: http://www.esat.kuleuven.ac.be/sista/lssvmlab/
  5. 5.
    Ukil, A., Jordaan, J.: A new approach to load forecasting: Using semi-parametric method and neural networks. In: King, I., Wang, J., Chan, L., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4233, pp. 974–983. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Suykens, J.: Support Vector Machines and Kernel Based Learning. Tutorial: IJCNN, Montreal (2003), [Online], Available from: http://www.esat.kuleuven.ac.be/sista/lssvmlab/ijcnn2005_4.pdf
  7. 7.
    Pai, P.F., Hong, W.C.: Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Elsevier Electric Power Systems Research 74, 417–425 (2005)CrossRefGoogle Scholar
  8. 8.
    Yang, J., Stenzel, J.: Short-term load forecasting with increment regression tree. Elsevier Electric Power Systems Research 76, 880–888 (2006)CrossRefGoogle Scholar
  9. 9.
    Zivanovic, R.: Analysis of Recorded Transients on 765kV Lines with Shunt Reactors. In: Power Tech2005 Conference, St. Petersburg, Russia (2005)Google Scholar
  10. 10.
    Zivanovic, R., Schegner, P., Seifert, O., Pilz, G.: Identification of the Resonant-Grounded System Parameters by Evaluating Fault Measurement Records. IEEE Transactions on Power Delivery 19, 1085–1090 (2004)CrossRefGoogle Scholar
  11. 11.
    Jordaan, J.A., Zivanovic, R.: Time-varying Phasor Estimation in Power Systems by Using a Non-quadratic Criterium. Transactions of the South African Institute of Electrical Engineers (SAIEE) 95, 35–41 (2004), ERRATA: 94(3), 171–172 (September 2004)Google Scholar
  12. 12.
    Gorry, P.A.: General Least-Squares Smoothing and Differentiation by the Convolution (Savitzky-Golay) Method.  62, 570–573 (1990)Google Scholar
  13. 13.
    Bialkowski, S.E.: Generalized Digital Smoothing Filters Made Easy by Matrix Calculations. 61, 1308–1310 (1989)Google Scholar
  14. 14.
    Draper, N.R., Smith, H.: Applied Regression Analysis, 2nd edn. John Wiley & Sons, Chichester (1981)zbMATHGoogle Scholar
  15. 15.
    Tibshirani, R.: Regression Shrinkage and Selection via the Lasso. Journal of the Royal Society. Series B (Methodological) 58, 267–288 (1996)zbMATHMathSciNetGoogle Scholar
  16. 16.
    Casar-Corredera, J.R., Alcásar-Fernándes, J.M., Hernándes-Gómez, L.A.: On 2-D Prony Methods. IEEE CH2118-8/85/0000-0796 $1.00, 796–799 (1985)Google Scholar
  17. 17.
    Zivanovic, R., Schegner, P.: Pre-filtering Improves Prony Analysis of Disturbance Records. In: Eighth International Conference on Developments in Power System Protection, Amsterdam, The Netherlands (2004)Google Scholar
  18. 18.
    Mathworks: MATLAB Documentation - Neural Network Toolbox. Version 6.5.0.180913a Release 13 edn. Mathworks Inc., Natick, MA (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • J. A. Jordaan
    • 1
  • A. Ukil
    • 2
  1. 1.Tshwane University of Technology, Staatsartillerie Road, Pretoria, 0001South Africa
  2. 2.ABB Corporate Research, Segelhofstrasse 1K, Baden Daettwil, CH-5404Switzerland

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