Real-Time Stream Mining Electric Power Consumption Data Using Hoeffding Tree with Shadow Features

  • Simon Fong
  • Meng Yuen
  • Raymond K. WongEmail author
  • Wei Song
  • Kyungeun Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)


Many energy load forecasting models have been established from batch-based supervised learning models where the whole data must be loaded to learn. Due to the sheer volumes of the accumulated consumption data which arrive in the form of continuous data streams, such batch-mode learning requires a very long time to rebuild the model. Incremental learning, on the other hand, is an alternative for online learning and prediction which learns the data stream in segments. However, it is known that its prediction performance falls short when compared to batch learning. In this paper, we propose a novel approach called Shadow Features (SF) which offer extra dimensions of information about the data streams. SF are relatively easy to compute, suitable for lightweight online stream mining.


Electric power consumption prediction Data stream mining Shadow features 



The authors are thankful for the financial support from the Research Grant Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF), Grant no. MYRG2015-00128-FST, offered by the University of Macau, FST, and RDAO.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Simon Fong
    • 1
  • Meng Yuen
    • 1
  • Raymond K. Wong
    • 2
    Email author
  • Wei Song
    • 3
  • Kyungeun Cho
    • 4
  1. 1.Department of Computer Information ScienceUniversity of MacauMacau SARChina
  2. 2.School of Computer Science and EngineeringUniversity of New South WalesKensingtonAustralia
  3. 3.College of Information EngineeringNorth China University of TechnologyBeijingChina
  4. 4.Department of Multimedia EngineeringDongguk UniversitySeoulSouth Korea

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