Carbon emissions forecasting based on tensor decomposition with multi-source data fusion

Xu, Xiaofeng; Cao, Xiaoxi; Yu, Lean

doi:10.1016/j.ins.2024.121235

Information Sciences

2024

DOI: 10.1016/j.ins.2024.121235

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Carbon emissions forecasting based on tensor decomposition with multi-source data fusion

Xiaofeng Xu,

Xiaoxi Cao,

Lean Yu

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2024

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MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy

Luo,

Shi,

Liu

et al. 2024

Environ. Res. Commun.

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With the development of the social economy and increasing energy consumption, carbon emissions become a matter of global concern. It's useful to forecast the trends in carbon emissions to assist policy-maker in formulating effective emission reduction strategies. In this paper, we propose a carbon emissions forecast model, which comprehensively considers the combined impact of multiple carbon emissions-related factors in China to conduct more accurate real-world conditions. Also, we employ a non-linear model that combines time offset with rolling time training method to optimize the non-linear characteristics of data. Moreover, we introduce a dynamic iterative biological metabolic model to handle time series data, effectively addressing data lag and errors. Experiments show our scheme has better performance in filtering out extraneous data, with an average relative residual of 0.055 and an average ratio bias of 0.049, compared with other schemes.

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MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy

Luo,

Shi,

Liu

et al. 2024

Environ. Res. Commun.

View full text Add to dashboard Cite

show abstract

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Carbon emissions forecasting based on tensor decomposition with multi-source data fusion

Cited by 1 publication

References 46 publications

MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy

MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy

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