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An improved grey model WD-TBGM (1, 1) for predicting energy consumption in short-term

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Abstract

The traditional grey model has been widely used for predicting energy consumption (EC) in short-term with a small sample-size, but its accuracy is greatly affected by data fluctuation. In order to further improve the prediction performance while considering the data fluctuation, in this study, the wavelet de-noising is introduced to pre-processing the EC data as the input of a modified grey model, leading to an improved novel grey model WD-TBGM (1, 1). It is found that using a wavelet decomposition algorithm can denoise the data and then the data fluctuation is effectively reduced. After illustrating effectiveness by numerical simulation and case study, the prediction performance of this newly proposed hybrid model can be enhanced with approximately 5% compared with the classical grey models. Furthermore, this newly proposed hybrid model is used to address the issues of EC prediction in China which is one of the worldwide top ten energy consumers and in Shanghai city which is one of the top energy consumers in China. The forecasting results show that the total EC of China and Shanghai will slow down in the next few years, which is in line with their actual development situation. This research also explains the effectiveness of the energy conservation and emission reduction policies that China and Shanghai are taking.

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Acknowledgements

The authors thank the anonymous reviewers for their valuable comments, which helped us to considerably improve the content, quality and presentation of this paper. Meanwhile, the authors are also very thankful to Dr. Qingtai Xiao (Kunming University of Science and Technology, China) and Dr. Meng Li (University of Texas at San Antonio, United States) for touching up our manuscript.

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Authors and Affiliations

  1. Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, 650093, People’s Republic of China

    Jie Li & Yelin Wang

  2. Robert C. Vackar College of Business and Entrepreneurship, University of Texas Rio Grande Valley, 1201 W University Dr, Edinburg, TX, 78539, USA

    Bin Li

Authors
  1. Jie Li
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  2. Yelin Wang
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  3. Bin Li
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Contributions

YW and BL are responsible for selecting data, providing analysis and prediction tools, and preparing the first draft. JL provides writing ideas and completes the final draft. All authors read and approved the final manuscript.

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Correspondence to Yelin Wang.

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Li, J., Wang, Y. & Li, B. An improved grey model WD-TBGM (1, 1) for predicting energy consumption in short-term. Energy Syst 13, 167–189 (2022). https://doi.org/10.1007/s12667-020-00410-y

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