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Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network

  • Kang Ke
  • Sun HongbinEmail author
  • Zhang Chengkang
  • Carl Brown
Special Issue
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Abstract

With the rapid development of smart grid, to solve the power enterprises’ requirement in short-term load forecasting, this paper proposes a short-term electrical load forecasting method based on stacked auto-encoding and GRU (Gated recurrent unit) neural network. Firstly, the method input historical data which contains power load, weather information, and holiday information, and use auto-encoding to compress the historical data; and then, the multi-layer GRU is used to construct the model to predict the power load. The experiment results show, compared with traditional models, the proposed method can effectively predict the daily variation of power load and have lower prediction error and higher precision.

Keywords

Smart grid Deep learning Electrical load forecasting Stacked auto-encoding 

Notes

Acknowledgements

This work was supported in part by the Scientific and Technological Planning Project of Jilin Province (20180101057JC). A Project Supported by Scientific and Technological Planning Project of Jilin Province (2018C0361).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Kang Ke
    • 1
  • Sun Hongbin
    • 2
    Email author
  • Zhang Chengkang
    • 1
  • Carl Brown
    • 3
  1. 1.Baicheng Power Supply CompanyState Grid Jilin Electric Power Company LimitedChangchunChina
  2. 2.School of Electrical EngineeringChangchun Institute of TechnologyChangchunChina
  3. 3.Department of Electrical EngineeringTexas A&M UniversityCollege StationUSA

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