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ORIGINAL RESEARCH
Research on the Evaluation and Prediction
of the Prefabricated Cabin Substations
Carbon Footprint Based on Life Cycle Theory
and an Extreme Learning Machine
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: 2024-08-19
Final revision date: 2024-09-23
Acceptance date: 2024-10-13
Online publication date: 2025-03-05
Publication date: 2025-11-14
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
Pol. J. Environ. Stud. 2025;34(6):8097-8114
KEYWORDS
TOPICS
ABSTRACT
Prefabricated cabin substations, as a new type of substation, have advantages such as saving
investment, a short construction period, and low-carbon environmental protection. They are
the mainstream development trend of low-carbon substations in the future and play an important
role in the construction of new power systems, especially in the development of urban power grids.
This article systematically conducted carbon footprint tracing, analysis, calculation, and evaluation
of prefabricated substations during the planning, construction, operation, and scrapping stages, forming
a carbon footprint accounting method for the entire life cycle of prefabricated substations. The research
results show that the carbon footprint of the construction and operation stages accounts for more than
90% of the carbon footprint of a prefabricated substation throughout its life cycle. Material carbon
footprint, SF6 carbon footprint, and station electricity carbon footprint are important components
of the substation’s carbon footprint. Green plants and lawns can effectively reduce the carbon emissions
of prefabricated substations and have a positive effect on controlling carbon footprints. Suggestions were
put forward to reduce the carbon footprint of prefabricated cabin substations in the areas of equipment
replacement, energy conservation and consumption reduction, operation monitoring, and optimization.
Simultaneously, carbon footprint prediction models for substations based on the bat algorithm (BA)
and extreme learning machine (ELM) were constructed to accurately predict the carbon footprint
of substations at various stages, providing a reference for the design selection, scheme optimization,
energy-saving, and carbon reduction transformation of prefabricated cabin substations.
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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