于志诚, 梁晔, 穆士才, 林华, 陈海洋. 基于聚类算法的代理购电工商业用户典型画像分析[J]. 电力大数据, 2024, 27(3): 57-63. DOI: 10.19317/j.cnki.1008-083x.2024.03.008
引用本文: 于志诚, 梁晔, 穆士才, 林华, 陈海洋. 基于聚类算法的代理购电工商业用户典型画像分析[J]. 电力大数据, 2024, 27(3): 57-63. DOI: 10.19317/j.cnki.1008-083x.2024.03.008
YU Zhi-cheng, LIANG Ye, MU Shi-cai, LIN Hua, CHEN Hai-yang. Analysis of Typical Portrait of Commercial Users of Electrician Purchasing Agent Based on Clustering Algorithm[J]. Power Systems and Big Data, 2024, 27(3): 57-63. DOI: 10.19317/j.cnki.1008-083x.2024.03.008
Citation: YU Zhi-cheng, LIANG Ye, MU Shi-cai, LIN Hua, CHEN Hai-yang. Analysis of Typical Portrait of Commercial Users of Electrician Purchasing Agent Based on Clustering Algorithm[J]. Power Systems and Big Data, 2024, 27(3): 57-63. DOI: 10.19317/j.cnki.1008-083x.2024.03.008

基于聚类算法的代理购电工商业用户典型画像分析

Analysis of Typical Portrait of Commercial Users of Electrician Purchasing Agent Based on Clustering Algorithm

  • 摘要: 对代理购电工商业电力用户用电特征的深入画像分析,将为电力电量预测、发用电计划安排、政策制度完善提供理论依据。本文获取了某地区分行业、分区域的代理购电工商业用户年、月、日等维度的用电数据,同步获取了与之匹配的气象、电价、经济、节假日安排等数据。通过数据处理算法对样本数据剔除离群点、补全缺失值,并建立用电量与温度、经济、用电价格、节假日之间的关联关系,构建全量代理购电工商业用户的特征向量矩阵。利用聚类算法对特征向量矩阵进行聚类分析,并应用划分完成的聚类簇对重点行业用户特征进行画像,画像结果可为实际生产工作提供参考。

     

    Abstract: The in-depth portrait analysis of the power consumption characteristics of commercial power users purchasing electricians will provide theoretical basis for power forecasting, power generation planning and policy system improvement. This paper obtains the annual, monthly and daily electricity consumption data of commercial electrician customers in different industries and regions, and synchronously obtains the matching meteorological, electricity price, economy, holiday arrangement and other data. Through the data processing algorithm, the outlier points are eliminated from the sample data, the missing values are filled out, and the correlation between electricity consumption and temperature, economy, electricity price and holidays is established, and the feature vector matrix of commercial customers who purchase electrical agents in full volume is constructed. The feature vector matrix is analyzed by clustering algorithm, and the user characteristics of key industries are sketched with the completed clustering. The sketching results can provide reference for practical production.

     

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