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A novel grey forecasting model and its application in forecasting the energy consumption in Shanghai

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Abstract

Prediction of energy consumption for a country (region) plays critical roles in economy and energy security, and accurate energy consumption forecasting is valuable for policy makers to formulate energy policies. To do this, we propose a novel improved GM(1,1) model, which is based on both data transformation for the original data sequence and optimization of the background value, and is therefore named as TBGM(1,1). TBGM(1,1) is employed to predict the total energy consumption of Shanghai City in China. And the results suggest that the TBGM(1,1) performs well compared with the traditional GM(1,1) model and other grey modification models in this context and Shanghai’s total energy consumption will increase stably in the following five years. In summary TBGM(1,1) proposed in our study has competent exploration and exploitation ability, and TBGM(1,1) could be utilized as an effective and promising tool for short-term planning, which can be applied for energy consumption forecasting in particular and for other forecasting issues as well.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (61572140), the Shanghai Municipal R&D Foundation (17DZ1100504 and 16511104704), and Graduate Student Innovation Fund Program of Shanghai University of Finance and Economics in 2017 (CXJJ-2017-423).The authors thank the anonymous reviewers for their valuable comments, which helped us to considerably improve the content, quality and presentation of this paper.

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  1. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Kai Li & Tao Zhang

  2. Shanghai Key Laboratory of Financial Information Technology, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Tao Zhang

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  1. Kai Li
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Li, K., Zhang, T. A novel grey forecasting model and its application in forecasting the energy consumption in Shanghai. Energy Syst 12, 357–372 (2021). https://doi.org/10.1007/s12667-019-00344-0

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