| 李顺昕,远振海,丁健民,岳云力,邓春宇,刘凤魁,张玉天,王新迎.基于聚类的用户用电行为及其影响因素分析[J].电力需求侧管理,2019,21(3):53-58 |
| 基于聚类的用户用电行为及其影响因素分析 |
| Analysisofusers’electricitybehaviorandinfluencingfactorsbasedonclustering |
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| DOI：10.3969/j.issn.1009-1831.2019.03.012 |
| 中文关键词: 用户行为分析 聚类 互信息 影响因素 |
| 英文关键词: users’behavior analysis clustering mutual information influencing factors |
| 基金项目: |
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| 摘要点击次数: 3449 |
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| 中文摘要: |
| 随着我国产业结构的不断调整，用户的用电特性也不断变化，且用户的用电行为逐渐向个性化发展。首先利用离散小波变换对用户负荷进行特征提取；然后利用改进快速密度峰值聚类算法进行聚类，根据用户特征聚类结果的不同，将用户分为不同群组，分析负荷群的时间分布特征；采用互信息方法分析用电量数据与经济、气温、行业关键指标等的相关性，提取出关键影响因素；最后，基于某省某行业典型用户的仿真实例验证了本文所提方法的有效性。 |
| 英文摘要: |
| With the constant adjustment of the industrial structure in China, the users’characteristics are changing, and the users’electricity behavior gradually develops into individuation.Firstly, the discrete wavelet transform is used to extract the characteristics of user load data. Secondly, the improved fast density peaks clustering algorithm is used to cluster the users into load groups with different power consumption behaviors, and then the time distribution characteristics of the load groups are analyzed.The mutual information method is used to analyze the correlation between electricity consumption data, economy, temperature, industry key indicators and so on, and the key influencing factors are extracted. Finally, the simulation results of a typical user in an industry in a province verify the effectiveness of the proposed method. |
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