文章摘要
郑 杨,王雨薇,徐丁吉,邓士伟,李莹莹,陈星瑞.考虑电能使用行为的用户碳画像研究[J].电力需求侧管理,2024,26(2):100-106
考虑电能使用行为的用户碳画像研究
Research on user carbon portrait considering power usage behavior
投稿时间:2023-10-15  修订日期:2024-01-03
DOI:10. 3969 / j. issn. 1009-1831. 2024. 02. 016
中文关键词: 碳画像  减碳负荷特性  低碳用电特性  电能产消碳特性  k-means聚类算法
英文关键词: carbon portrait  carbon load reduction characteristics  low carbon electricity characteristics  carbon consumption of electric energy  k-means clustering algorithm
基金项目:国网江苏省电力有限公司科技项目资助(J2022156)
作者单位
郑 杨 国网江苏省电力有限公司 镇江供电分公司,江苏 镇江 212000 
王雨薇 国网江苏省电力有限公司 镇江供电分公司,江苏 镇江 212000 
徐丁吉 国网江苏省电力有限公司 镇江供电分公司,江苏 镇江 212000 
邓士伟 江苏智臻能源科技有限公司,南京 211100 
李莹莹 江苏智臻能源科技有限公司,南京 211100 
陈星瑞 江苏智臻能源科技有限公司,南京 211100 
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中文摘要:
      随着我国提出“碳达峰、碳中和”的双碳建设目标,节能减碳成为当下的热点问题。用户碳画像对电力公司分析居民用户的电能使用行为,制定合理的电能减碳调控措施具有重要意义。为此,对基于电能使用行为的用户碳画像标签体系和画像方法进行研究。首先对用户多维用能数据进行搜集和筛选;然后根据筛选后的用户电能使用行为数据,结合画像目的,从用户减碳负荷特性、用户低碳用电特性、用户电能产消碳特性3个维度设计用户碳画像的标签体系;再将子标签进行数据处理并对各类综合指标分析得到各维标签数据。最后通过使用k-means聚类算法判断并确定用户的所属簇别,以三维散点图进行展示;再根据标签体系计算获得用户的减碳综合指数并以柱状图进行展示,实现了用户碳画像的可视化呈现,反映了用户电能减碳的综合能力;通过选取典型用户对比实施减碳措施前后的碳排放量,验证了所提方法的有效性。
英文摘要:
      With the dual carbon construction target of“carbon peak, carbon neutralization”proposed in our country, energy saving and carbon reduction has become a hot issue instantly. User portraits are of great significance for power companies to analyze the power usage behavior of residents and formulate reasonable regulation measures for energy carbon reduction. For this, user carbon portrait label system and portrait method based on power usage behavior are studied. Firstly, collect and filter user multidimensional behavior data. Secondly,according to the filtered data of user’s electricity usage behavior, combined with the purpose of portrait, the label system of user’s carbon portrait is designed from three dimensions:carbon load reduction characteristics of users, low-carbon electricity consumption characteristics of users, carbon production and consumption characteristics of users’electric energy. The data of the sublabels are processed and the multidimensional label data is obtained by analyzing various comprehensive indexes. Finally, by using the k-means clustering algorithm to identify the user’s cluster, and displayed with the three-dimensional scatter plot, the user’s comprehensive carbon reduction index is calculated according to the label system and displayed in a column chart, realizes the visual presentation of the user’s carbon portrait, and reflects the comprehensive ability of the user’s electric energy carbon reduction. The effectiveness of the proposed method is demonstrated by comparing the carbon emissions of typical users before and after the implementation of carbon reduction measures.
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