Advertisement

Deep Structure of Gaussian Kernel Function Networks for Predicting Daily Peak Power Demands

  • Dae Hyeon Kim
  • Ye Jin Lee
  • Rhee Man Kil
  • Hee Yong Youn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)

Abstract

This paper proposes a novel method of predicting daily peak power demands using the deep structure of Gaussian kernel function networks (GKFNs). For the prediction model, the whole time series is divided into multiple parts and each part is trained using a GKFN. Then, the trained GKFNs are combined using the deep structure of GKFNs to minimize the mean square errors (MSEs) of prediction model. As a consequence, the proposed deep structure of GKFNs provides an improved performance of prediction accuracy compared with canonical GKFNs. The simulation for predicting daily peak power demands in Korea reveals that the proposed prediction model has the merits in prediction performances compared with the GKFN model and also other prediction models such as the k-NN and SVR.

Keywords

Daily peak power demand Prediction model Gaussian kernel function network Deep structure 

Notes

Acknowledgment

This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-00133, Research on Edge computing via collective intelligence of hyperconnection IoT nodes).

References

  1. 1.
    Srinivasan, D., Lee, M.A.: Survey of hybrid fuzzy neural approaches to electric load forecasting. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 4004–4008. IEEE Press, New York (1995)Google Scholar
  2. 2.
    Srivastava, A.K., Pandey, A.S., Singh, D.: Short-term load forecasting methods: a review. In: IEEE International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems, pp. 130–138. IEEE Press, New York (2016)Google Scholar
  3. 3.
    Papalexopoulos, A.D., Hesterberg, T.C.: A regression-based approach to short-term system load forecasting. IEEE Trans. Power Syst. 5(4), 1535–1547 (1990)CrossRefGoogle Scholar
  4. 4.
    Amjady, N.: Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans. Power Syst. 16(4), 798–805 (2001)CrossRefGoogle Scholar
  5. 5.
    Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.: Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 6(2), 442–449 (1991)CrossRefGoogle Scholar
  6. 6.
    Lee, K.Y., Cha, Y.T., Park, J.H.: Short-term load forecasting using an artificial neural network. IEEE Trans. Power Syst. 7(1), 124–132 (1992)CrossRefGoogle Scholar
  7. 7.
    Rahman, S., Bhatnagar, R.: An expert system based algorithm for short term load forecast. IEEE Trans. Power Syst. 3(2), 392–399 (1988)CrossRefGoogle Scholar
  8. 8.
    Fan, S., Chen, L.: Short-term load forecasting based on an adaptive hybrid method. IEEE Trans. Power Syst. 21(1), 392–401 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kim, K.H., Park, J.K., Hwang, K.J., Kim, S.H.: Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Trans. Power Syst. 10(3), 1534–1539 (1995)CrossRefGoogle Scholar
  10. 10.
    Kil, R.: Function approximation based on a network with kernel functions of bounds and locality. ETRI J. 15, 35–51 (1993)CrossRefGoogle Scholar
  11. 11.
    Packard, N.H., Crutchfield, J.P., Farmer, J.D., Shaw, R.S.: Geometry from a time series. Phys. Rev. Lett. 45, 712–716 (1980)CrossRefGoogle Scholar
  12. 12.
    Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. LNM, vol. 898, pp. 366–381. Springer, Heidelberg (1981).  https://doi.org/10.1007/BFb0091924CrossRefGoogle Scholar
  13. 13.
    Havstad, J.W., Ehlers, C.L.: Attractor dimension of nonstationary dynamical systems from small data sets. Phys. Rev. A 39(2), 845–853 (1989)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kil, R., Park, S., Kim, S.: Time series analysis based on the smoothness measure of mapping in the phase space of attractors. In: International Joint Conference on Neural Networks, vol. 4, pp. 2584–2589. IEEE Press, New York (1999)Google Scholar
  15. 15.
    Kim, D.K., Kil, R.M.: Stock price prediction based on a network with gaussian kernel functions. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8227, pp. 705–712. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-42042-9_87CrossRefGoogle Scholar
  16. 16.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dae Hyeon Kim
    • 1
  • Ye Jin Lee
    • 1
  • Rhee Man Kil
    • 1
  • Hee Yong Youn
    • 1
  1. 1.College of Software, Sungkyunkwan UnivesitySuwonKorea

Personalised recommendations