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A Novel FD3 Framework for Carbon Emissions Prediction

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Abstract

Monitoring and controlling the carbon emissions need machine learning-based forecasting models at this modern era. Despite various of artificial neural networks (ANNs), we propose a novel FD3 framework to tackle carbon emissions prediction. In our approach, three “FD” procedures are executed: (1) frequency decomposition achieved by using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an advanced version of the famous empirical mode decomposition (EMD); (2) forecasting dendritic neuron model (DNM) that has proved validity on numerous prediction tasks, showing advanced nonlinear fitting ability than traditional network-structured ANNs; and (3) fluctuation density measurement (FD function) that used to regulate the predicting strategy for each decomposed subseries. In experiments, the FD3 framework has shown better performance than seven baseline models in terms of three widely used time series prediction evaluation metrics. The success of our FD3 has confirmed the validity of “preprocessing-forecasting” workflow and provides better solutions for carbon emissions prediction. Furthermore, the design of FD function can give more insights for signal analysis that the selection of decomposed subseries can have huge impacts on the original data.

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Data Availability

The data used in this work can be accessed at https://carbonmonitor.org.

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Funding

This research was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant JP22H03643, Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING) under Grant JPMJSP2145, and JST through the Establishment of University Fellowships toward the Creation of Science Technology Innovation under Grant JPMJFS2115.

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Authors and Affiliations

  1. Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Toyama-ken, Japan

    Houtian He, Tongyan Liu, Qianqian Li, Jiaru Yang & Shangce Gao

  2. Faculty of Engineering, University of Fukui, Fukui-shi, 910-8507, Fukui-ken, Japan

    Rong-Long Wang

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  1. Houtian He
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  2. Tongyan Liu
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Contributions

Houtian He: writing—original draft, methodology, conceptualization. Tongyan Liu: programming, writing—review and editing, visualization. Qianqian Li: writing—review and editing. Jiaru Yang: writing—review and editing. Rong-Long Wang: writing—reviewing and editing, supervision. Shangce Gao: writing—review and editing, resources, project administration, supervision.

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He, H., Liu, T., Li, Q. et al. A Novel FD3 Framework for Carbon Emissions Prediction. Environ Model Assess 29, 455–469 (2024). https://doi.org/10.1007/s10666-023-09918-w

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