Energy feature model based on feature vector set

Ke Chen; Zhangchi Ying; Yuqing Xie; Chengxin Zhang

doi:10.1117/12.2640068

17 May 2022 Energy feature model based on feature vector set

Ke Chen, Zhangchi Ying, Yuqing Xie, Chengxin Zhang

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122593U (2022) https://doi.org/10.1117/12.2640068
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

The data of energy consumption structure belongs to compositional data. The traditional prediction model is difficult to realize the effective prediction of the data series of energy consumption structure, especially the energy consumption structure prediction problem based on the characteristics of poor information that lacks an effective model. The modified Fourier-Markov GM (1,1) model proposed in this paper can effectively predict and analyze the development trend of China's energy consumption system structure. Considering the transformation processing method of component data, a group of ordinary series is generated by coordinate transformation, and then the GM (1,1) model is constructed to predict the system structure. The model is used to predict the development trend of energy consumption system structure in the next few years, and to provide suggestions for possible problems, such as advocating energy conservation and emission reduction, promoting energy diversification, improving energy-related technologies, and implementing energy policies and regulations.

Citation Download Citation

Ke Chen, Zhangchi Ying, Yuqing Xie, and Chengxin Zhang "Energy feature model based on feature vector set", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122593U (17 May 2022); https://doi.org/10.1117/12.2640068

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