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

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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.

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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.

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China

    Binbin Yong, Xin Wang & Qingguo Zhou

  2. School of Physical Science and Technology, Lanzhou University, Lanzhou, Gansu, China

    Binbin Yong & Liang Huang

  3. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2522, Australia

    Fucun Li & Jun Shen

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  1. Binbin Yong
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  2. Liang Huang
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  3. Fucun Li
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  4. Jun Shen
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  5. Xin Wang
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  6. Qingguo Zhou
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Correspondence to Qingguo Zhou.

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Yong, B., Huang, L., Li, F. et al. A research of Monte Carlo optimized neural network for electricity load forecast. J Supercomput 76, 6330–6343 (2020). https://doi.org/10.1007/s11227-019-02828-3

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  • DOI: https://doi.org/10.1007/s11227-019-02828-3

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