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Short-Term Electricity Demand Forecasting Based on Multiple LSTMs

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Advances in Brain Inspired Cognitive Systems (BICS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11691))

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Abstract

In recent years, the problem of unbalanced demand and supply in electricity power industry has seriously affected the development of smart grid, especially in the capacity planning, power dispatching and electric power system control. Electricity demand forecasting, as a key solution to the problem, has been widely studied. However, electricity demand is influenced by many factors and nonlinear dependencies, which makes it difficult to forecast accurately. On the other hand, deep neural network technologies are developing rapidly and have been tried in time series forecasting problems. Hence, this paper proposes a novel deep learning model, which is based on the multiple Long Short-Term Memory (LSTM) neural networks to solve the problem of short-term electricity demand forecasting. Compared with autoregressive integrated moving average model (ARIMA) and back propagation neural network (BPNN), our model demonstrates competitive forecast accuracy, which proves that our model is promising for electricity demand forecasting.

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Acknowledgment

This work was supported by National Natural Science Foundation of China under Grant No. 61402210 and 60973137, 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.

Author information

Authors and Affiliations

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

    Binbin Yong, Zebang Shen, Yongqiang Wei & Qingguo Zhou

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

    Binbin Yong

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

    Jun Shen

  4. Department of EE and CS, Research Lab of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Jun Shen

Authors
  1. Binbin Yong
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  2. Zebang Shen
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  3. Yongqiang Wei
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  4. Jun Shen
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  5. Qingguo Zhou
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Corresponding author

Correspondence to Qingguo Zhou .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  4. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Kaizhu Huang

  5. Northwestern Polytechnical University, Xi'an, China

    Jiangbin Zheng

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  7. Guangdong Polytechnic Normal University, Guangzhou, China

    Rongjun Chen

  8. Guangdong Polytechnic Normal University, Guangzhou, China

    Yinyin Xiao

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Yong, B., Shen, Z., Wei, Y., Shen, J., Zhou, Q. (2020). Short-Term Electricity Demand Forecasting Based on Multiple LSTMs. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_18

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  • DOI: https://doi.org/10.1007/978-3-030-39431-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39430-1

  • Online ISBN: 978-3-030-39431-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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