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Journal of Electrical Engineering & Technology

, Volume 14, Issue 6, pp 2315–2322 | Cite as

Improved Elman Neural Network Short-Term Residents Load Forecasting Considering Human Comfort Index

  • Yunjun YuEmail author
  • Xianzheng Wang
  • Roland Bründlinger
Original Article
  • 5 Downloads

Abstract

The massive access of distributed power in distribution network increases the complexity of user’s power consumption mode. It puts higher requirements on the accuracy and stability of load forecasting. The forward neural network has limitation in dynamic performance, and the prediction accuracy needs to be improved. This paper considers the influence of daily feature correlation factors on residential load. Then a method for improved Elman neural network short-term residential load forecasting considering human comfort index is designed. Using the human comfort index overcomes the shortcomings of low accuracy in load prediction when meteorological factor as a direct input. The Elman neural network’s incentive function is improved. The softmax function serves as an incentive function for hidden layer. Short-term load forecasting model was established for the load of Nanchang, Jiangxi, China. In order to reduce the impact of residents’ load characteristics, the samples of load are divided into weekend load, seasonal load and typical weather type load. Experiments show that the improved Elman neural network has higher prediction accuracy under three load types, compared with Elman neural network and RBF neural network.

Keywords

Improved Elman neural network Short-term residential load forecasting Human comfort index Incentive function RBF neural network 

Notes

Acknowledgements

This work was supported by the Program of China International Science and Technology Cooperation Projects (2014DFG72240). Project Supported by National Natural Science Foundation of China (61563034) and Double creative team in Jiangsu province 2016. This work was supported by the Nanchang University Graduate Innovation Special Fund Project (CX2018155).

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Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Yunjun Yu
    • 1
    Email author
  • Xianzheng Wang
    • 1
  • Roland Bründlinger
    • 2
  1. 1.School of Information EngineeringNanchang UniversityNanchangChina
  2. 2.AIT Austrian Institute of Technology GmbHSeibersdorfAustria

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