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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
An Optimization Framework for Monitoring
and Predicting Electricity Carbon Emissions
Based on Sparrow Search Algorithm
and Improved BPNN Neural Network
1
Guangzhou Power Supply Bureau of Guangdong Grid Co, GuangZhou 510600, China
2
Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group,
Wuhan 430064, China
Submission date: 2025-06-18
Final revision date: 2025-10-11
Acceptance date: 2025-12-28
Online publication date: 2026-02-24
Corresponding author
Junjian Zhang
Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430064, China
Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430064, China
KEYWORDS
carbon emission predictioncarbon emission factorselectricity consumptionBP neural networksparrow algorithm
TOPICS
ABSTRACT
In recent years, abnormal weather has occurred frequently, and effectively controlling and
reducing carbon emissions has become one of the important challenges currently faced by society.
With the acceleration of electricity grid construction, a large number of new energy projects
are gradually connected to the grid, and the electricity grid system is developing toward a clean
and low-carbon direction. The prediction of power carbon emissions has enabled quantitative research
on power system carbon emissions, and understanding the changing trend of power carbon emissions
is of great significance for promoting the decarbonization of power systems.
This study constructs a PE-PACF-SSA-BPNN combined prediction model for electricity carbon emissions. First, external factors affecting electricity carbon emissions are selected based on the Pearson coefficient (PE), and partial autocorrelation analysis (PACF) is conducted on the electricity carbon emission sequence. The internal influencing factors of electricity carbon emissions are selected based on partial autocorrelation coefficients, which reflect the changing trends and patterns of electricity carbon emissions. Second, a BPNN neural network is used to model the correlation between electricity carbon emissions and influencing factors, and the BPNN parameters are updated through an error-propagation mechanism. Carbon emission prediction is realized based on influencing factors and the BPNN neural network.
Simultaneously, the Sparrow Search Algorithm (SSA) is used to optimize the weights between the input layer and the hidden layer of the BPNN to improve model robustness. The prediction errors of this model in four scenarios – China, Guangdong, Shandong, and Jiangsu – are 0.628%, 2.924%, 1.852%, and 1.321%, respectively. This research constructs a novel tool and method for carbon emission prediction in the power industry, which is helpful for the tracking and prediction of the carbon footprint of the power industry and provides a reference for its clean development.
This study constructs a PE-PACF-SSA-BPNN combined prediction model for electricity carbon emissions. First, external factors affecting electricity carbon emissions are selected based on the Pearson coefficient (PE), and partial autocorrelation analysis (PACF) is conducted on the electricity carbon emission sequence. The internal influencing factors of electricity carbon emissions are selected based on partial autocorrelation coefficients, which reflect the changing trends and patterns of electricity carbon emissions. Second, a BPNN neural network is used to model the correlation between electricity carbon emissions and influencing factors, and the BPNN parameters are updated through an error-propagation mechanism. Carbon emission prediction is realized based on influencing factors and the BPNN neural network.
Simultaneously, the Sparrow Search Algorithm (SSA) is used to optimize the weights between the input layer and the hidden layer of the BPNN to improve model robustness. The prediction errors of this model in four scenarios – China, Guangdong, Shandong, and Jiangsu – are 0.628%, 2.924%, 1.852%, and 1.321%, respectively. This research constructs a novel tool and method for carbon emission prediction in the power industry, which is helpful for the tracking and prediction of the carbon footprint of the power industry and provides a reference for its clean development.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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