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Nonlinear Dynamics

, Volume 98, Issue 2, pp 1107–1136 | Cite as

Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm

  • Zichen Zhang
  • Wei-Chiang HongEmail author
Original paper
  • 59 Downloads

Abstract

Accurate electric load forecasting can provide critical support to makers of energy policy and managers of power systems. The support vector regression (SVR) model can be hybridized with novel meta-heuristic algorithms not only to identify fluctuations and the nonlinear tendencies of electric loads, but also to generate satisfactory forecasts. However, many such algorithms have numerous drawbacks, such as a low population diversity and trapping at local optima, which are problems of premature convergence. Accordingly, approaches to increase the accuracy of forecasting must be developed. In this investigation, quantum computing mechanism is used to quantamize dragonfly behaviors to enhance the searching effectiveness of the dragonfly algorithm, namely QDA. In addition, conducting the data preprocessing by the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) is useful to improve the forecasting accuracy. Thus, a new electric load forecasting model, the CEEMDAN-SVRQDA model, that combines the CEEMDAN and hybridizes the QDA with an SVR model, is proposed to provide more accurate forecasts. Two numerical examples from the Tokyo Electric Power Company (Japan) and the National Grid (UK) demonstrate that the proposed model outperforms other models.

Keywords

Quantum dragonfly algorithm (QDA) Support vector regression (SVR) Quantum computing mechanism (QCM) Complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) Electric load forecasting 

Notes

Acknowledgements

This research was conducted with the support from Jiangsu Normal University (No. 9213618401), China.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyJiangsu Normal UniversityXuzhouChina

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