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A research of Monte Carlo optimized neural network for electricity load forecast

  • Binbin Yong
  • Liang Huang
  • Fucun Li
  • Jun Shen
  • Xin Wang
  • Qingguo ZhouEmail author
Article
  • 55 Downloads

Abstract

In this paper, we apply the Monte Carlo neural network (MCNN), a type of neural network optimized by Monte Carlo algorithm, to electricity load forecast. Meanwhile, deep MCNNs with one, two and three hidden layers are designed. Results have demonstrated that three-layer MCNN improves 70.35% accuracy for 7-week electricity load forecast, compared with traditional neural network. And five-layer MCNN improves 17.24% accuracy for 7-week forecast. This proves that MCNN has great potential in electricity load forecast.

Keywords

Monte Carlo Neural network Electricity load forecast Deep MCNNs 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant Nos. 61402210 and 60973137, Ministry of Education—China Mobile Research Foundation under Grant No. MCM20170206, State Grid Corporation Science and Technology Project under Grant No. SGGSKY00FJJS1700302, Program for New Century Excellent Talents in University under Grant No. NCET-12-0250, Major National Project of High Resolution Earth Observation System under Grant No. 30-Y20A34-9010-15/17, Strategic Priority Research Program of the Chinese Academy of Sciences with Grant No. XDA03030100, Google Research Awards and Google Faculty Award.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest regarding the publication of this manuscript.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Physical Science and TechnologyLanzhou UniversityLanzhouChina
  3. 3.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia

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