Abstract
Forecasting the electricity consumption has always played an important role in the management of power system management, which requires higher forecasting technology. Therefore, based on the principle of “new information priority”, combined with rolling mechanism and Markov theory, a novel grey power-Markov prediction model with time-varying parameters (RGPMM(λ,1,1)) is designed, which overcomes the inherent defects of fixed structure and poor adaptability to the changes of original data. In addition, in order to prove the validity and applicability of the prediction model, we have used the model to predict China’s total electricity consumption, and have compared it with the prediction results by a series of benchmark models. The result shows that the can better adapt to the characteristics of electricity consumption data, and it also shows the advantages of the proposed forecasting model. In this paper, the proposed forecasting model is used to predict China’s total electricity consumption in the next six years from 2018 to 2023, so as to provide certain reference value for power system management and distribution.
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Acknowledgements
The authors are grateful to the editor and reviewers for their valuable comments.
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This work is financially supported by the National Natural Science Foundation of China (61573266) and Natural Science Basic Research Program of Shaanxi (Program No. 2021JM-133).
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Responsible Editor: Philippe Garrigues
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Liqin Sun contributed the idea. Liqin Sun and Youlong Yang performed the calculations. Jiadi Zhu checked the calculations. Liqin Sun wrote the main manuscript. Youlong Yang and Tong Ning made an improvement. All authors contributed to discussion and reviewed the manuscript.
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Sun, L., Yang, Y., Ning, T. et al. A novel grey power-Markov model for the prediction of China’s electricity consumption. Environ Sci Pollut Res 29, 21717–21738 (2022). https://doi.org/10.1007/s11356-021-17016-1
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DOI: https://doi.org/10.1007/s11356-021-17016-1