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

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


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.


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



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).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

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