International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

  • Yu, Jungwon (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Sungshin (Department of Electrical and Computer Engineering, Pusan National University)
  • Received : 2016.03.11
  • Accepted : 2016.03.24
  • Published : 2016.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

Keywords

References

  1. T. Senjyu, H. Takara, K. Uezato, and T. Funabashi, "Onehour- ahead load forecasting using neural network," IEEE Transactions on Power Systems, vol. 17, no. 1, pp. 113-118, 2002. http://dx.doi.org/10.1109/59.982201
  2. E. E. Elattar, J. Goulermas, and Q. H. Wu, "Electric load forecasting based on locally weighted support vector regression," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 4, pp. 438-447, 2010. http://dx.doi.org/10.1109/TSMCC.2010.2040176
  3. S. Fan and L. Chen, "Short-term load forecasting based on an adaptive hybrid method," IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 392-401, 2006. http://dx.doi.org/10.1109/TPWRS.2005.860944
  4. J. Nagi, K. S. Yap, F. Nagi, S. K. Tiong, and S. K. Ahmed, "A computational intelligence scheme for the prediction of the daily peak load," Applied Soft Computing, vol. 11, no. 8, pp. 4773-4788, 2011. http://dx.doi.org/10.1016/j.asoc.2011.07.005
  5. B. Wang, N. L. Tai, H. Q. Zhai, J. Ye, J. D. Zhu, and L. B. Qi, "A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting," Electric Power Systems Research, vol. 78, no. 10, pp. 1679-1685, 2008. http://dx.doi.org/10.1016/j.epsr.2008.02.009
  6. D. J. Trudnowski, W. L. McReynolds, and J. M. Johnson, "Real-time very short-term load prediction for powersystem automatic generation control," IEEE Transactions on Control Systems Technology, vol. 9, no. 2, pp. 254-260, 2001. http://dx.doi.org/10.1109/87.911377
  7. S. J. Huang and K. R. Shih, "Short-term load forecasting via ARMA model identification including non- Gaussian process considerations," IEEE Transactions on Power Systems, vol. 18, no. 2, pp. 673-679, 2003. http://dx.doi.org/10.1109/TPWRS.2003.811010
  8. J. F. Chen, W. M. Wang, and C. M. Huang, "Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting," Electric Power Systems Research, vol. 34, no. 3, pp. 187-196, 1995. http://dx.doi.org/10.1016/0378-7796(95)00977-1
  9. N. Kandil, R. Wamkeue, M. Saad, and S. Georges, "An efficient approach for short term load forecasting using artificial neural networks," International Journal of Electrical Power & Energy Systems, vol. 28, no. 8, pp. 525-530, 2006. http://dx.doi.org/10.1016/j.ijepes.2006.02.014
  10. T. S. Dillon, S. Sestito, and S. Leung, "Short term load forecasting using an adaptive neural network," International Journal of Electrical Power & Energy Systems, vol. 13, no. 4, pp. 186-192, 1991. http://dx.doi.org/10.1016/0142-0615(91)90021-M
  11. A. J. R. Reis and A. P. A. da Silva, "Feature extraction via multiresolution analysis for short-term load forecasting," IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 189-198, 2005. http://dx.doi.org/10.1109/TPWRS.2004.840380
  12. R. K. Mehra, "Group method of data handling (GMDH): review and experience," in Proceedings of 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications, New Orleans, LA, 1977, pp. 29-34. http://dx.doi.org/10.1109/CDC.1977.271540
  13. B. Zhu, C. Z. He, P. Liatsis, and X. Y. Li, "A GMDH-based fuzzy modeling approach for constructing TS model," Fuzzy Sets and Systems, vol. 189, no. 1, pp. 19-29, 2012. http://dx.doi.org/10.1016/j.fss.2011.08.004
  14. A. G. Ivakhnenko, "Polynomial theory of complex systems," IEEE Transactions on Systems, Man, and Cybernetics, vol. 1, no. 4, pp. 364-378, 1971. http://dx.doi.org/ 10.1109/TSMC.1971.4308320
  15. J. A. Muller and F. Lemke, Self-organising Data Mining: Extracting Knowledge from Data. Hamburg: Books on Demand, 2000.
  16. H. R. Madala and A. G. Ivakhnenko, Inductive Learning Algorithms for Complex Systems Modeling. Boca Raton, FL: CRC Press, 1994.
  17. T. C. Hsia, System Identification: Least-Squares Methods. Lexington, MA: Lexington Books, 1977.
  18. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ: Prentice Hall, 1997.
  19. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. New York, NY: Press Syndicate of the University of Cambridge, 1992.
  20. R. L. Burden and J. D. Faires, Numerical Analysis, 5th ed. Boston: Cengage Learning, 1993.
  21. G. Strang, Linear Algebra and Its Applications. Belmont, CA: Thomson-Brooks/Cole, 2005.
  22. C. G. Atkeson, A. W. Moore, and S. Schaal, "Locally weighted learning," Artificial Intelligence Review, vol. 11, no. 1, pp. 11-73, 1997. http://dx.doi.org/10.1023/A:1006559212014
  23. H. Leung, Y. Huang, and C. Cao, "Locally weighted regression for desulphurisation intelligent decision system modeling," Simulation Modelling Practice and Theory, vol. 12, no. 6, pp. 413-423, 2004. http://dx.doi.org/10.1016/j.simpat.2004.06.002
  24. A. P. Engelbrecht, Computational Intelligence: An Introduction, 2nd ed. Chichester: John Wiley & Sons, 2007.
  25. C. C. Chang and C. J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article no. 27, 2011. http://dx.doi.org/10.1145/1961189.1961199

Cited by

  1. A novel approach for optimizing climate features and network parameters in rainfall forecasting pp.1433-7479, 2017, https://doi.org/10.1007/s00500-017-2756-7
  2. Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy vol.9, pp.1, 2019, https://doi.org/10.3390/app9010126