Algorithm for carbon emission peak prediction and emission reduction potential analysis based on convolutional neural network and multi-dimensional power big data

Xuwei Xia; Hongwei Han; Wang Su; Jiangbo Sha; Jia Liu

doi:10.1117/12.3033162

5 July 2024 Algorithm for carbon emission peak prediction and emission reduction potential analysis based on convolutional neural network and multi-dimensional power big data

Xuwei Xia, Hongwei Han, Wang Su, Jiangbo Sha, Jia Liu

Author Affiliations +

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318445 (2024) https://doi.org/10.1117/12.3033162
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In order to promote the green and low-carbon transformation of power industry, an algorithm for carbon emission peak prediction and emission reduction potential analysis based on convolutional neural network and multi-dimensional power big data is proposed. Under the convolution neural network and multi-dimensional power big data, the carbon emission data are normalized to obtain the reconstructed carbon emission data characteristics; The cyclic unit structure of convolutional neural network is constructed, and the data is processed by input gate, forgetting gate and output gate, so as to realize the peak carbon emission prediction, calculate the sum of the effects of four related factors, such as energy consumption, resource scale, resource distribution and climate conditions, and construct an analysis algorithm for carbon emission reduction potential. The experimental results show that the algorithm can effectively extract relevant information from the data, and establish a close relationship between carbon emission peak data and emission reduction potential data. The prediction result of carbon emission peak is basically consistent with the actual situation, with smaller mean square error and average absolute error, and higher prediction accuracy and stability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuwei Xia, Hongwei Han, Wang Su, Jiangbo Sha, and Jia Liu "Algorithm for carbon emission peak prediction and emission reduction potential analysis based on convolutional neural network and multi-dimensional power big data", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318445 (5 July 2024); https://doi.org/10.1117/12.3033162

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KEYWORDS

Carbon

Convolutional neural networks

Evolutionary algorithms

Education and training

Error analysis

Data processing

Feature extraction

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