计算技术与自动化2025，Vol.44Issue(3)：19-23,5.DOI:10.16339/j.cnki.jsjsyzdh.202503004
基于电-碳转换因子的工业用户用电信息分级挖掘算法
Hierarchical Mining Algorithm for Industrial Users' Electricity Consumption Information Based on Electric-carbon Conversion Factor
杨琦 1舒一飞 1梁飞 1康洁滢 1刘鹏1
作者信息
- 1. 国网宁夏电力有限公司营销服务中心(国网宁夏电力有限公司计量中心),宁夏银川 750011
- 折叠
摘要
Abstract
In order to reduce carbon emission,a hierarchical mining algorithm for industrial users' electricity consump-tion information based on electric-carbon conversion factor is studied to improve the comprehensiveness of industrial users' electricity consumption information hierarchical mining.The power flow tracking method is used to calculate the carbon emission of industrial users by combining the electricity consumption information.The power consumption of industrial users is obtained by using the bidirectional long and short term cyclic memory cyclic neural network combined with the power con-sumption information of industrial users.According to carbon emission and electricity consumption,the electric-carbon con-version factor of industrial users is obtained to reflect the correlation degree between electricity consumption and carbon e-mission.By using the fuzzy C-means clustering algorithm,the industrial users with similar elec tric-carbon conversion factors are classified into the same level to complete the hierarchical mining of industrial users' electricity consumption information.Experimental results show that the algorithm can effectively calculate the carbon emission and electricity consumption of in-dustrial users,and obtain the electric-carbon conversion factor.This algorithm can effectively mine the power consumption information of industrial users and determine the carbon conversion factor level of industrial users.关键词
电-碳转换因子/工业用户/用电信息/分级挖掘/潮流追踪法/模糊C均值聚类Key words
electric-carbon conversion factor/industrial users/electricity consumption information/hierarchical mining/trend tracking method/fuzzy C-means clustering分类
信息技术与安全科学引用本文复制引用
杨琦,舒一飞,梁飞,康洁滢,刘鹏..基于电-碳转换因子的工业用户用电信息分级挖掘算法[J].计算技术与自动化,2025,44(3):19-23,5.
