| 胡丹蕾,赵 冬,姜世公,王云飞,张重阳,柳 伟.基于大数据挖掘的终端用户供电需求划分方法[J].电力需求侧管理,2023,25(4):66-72 |
| 基于大数据挖掘的终端用户供电需求划分方法 |
| Power supply demand division method for end users based on big data mining |
| 投稿时间：2023-02-10 修订日期：2023-04-13 |
| DOI：10. 3969 / j. issn. 1009-1831. 2023. 04. 011 |
| 中文关键词: 大数据 终端用户 差异化供电需求 K-means DBSCAN 改进灰色关联度 |
| 英文关键词: big data end user differentiated supply demand K-means DBSCAN improving grey relational degree |
| 基金项目:国家电网有限公司科技项目（5400-202012118A-0-0-00） |
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| 摘要点击次数: 701 |
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| 中文摘要: |
| 针对不同供电区域多类型终端用户对供电可靠性的差异化需求以及目前配网侧依据单一供电可靠率指标衡量用户供电可靠性的局限性，提出一种基于大数据挖掘的终端用户的供电需求划分方法。首先，进行终端用户差异化供电需求量化，构建终端用户供电需求信息模型；然后，采用综合K-means 与基于密度聚类（density- based spatial clustering ofapplications with noise，DBSCAN）的大数据挖掘进行终端用户的聚类，实现用户的分类；最后采用改进的灰色关联度实现区域内终端用户可靠性等级划分。通过对含多类型终端用户的供电区域仿真分析，结合对比方案分析，进一步验证所提出的基于大数据挖掘的终端用户的供电可靠性需求划分方法的有效性。 |
| 英文摘要: |
| In view of different demands for power supply reliability of multi-type end users in different regions and the limitation of measuring user power supply reliability based on a single power supply reliability index at the distribution network side, a power supply demand division method for end users based on big data mining is proposed. Firstly, the differentiated power supply demand of endusers is quantified, and the information model of end-users’powersupply demand is constructed. Then, using integrated K-means and density- based spatial clustering of applications with noise big data mining to cluster end users, to achieve user classification. Finally,improved grey relational degree is used to divide the end user reliability level within the region. Through the simulation analysis of the power supply area with multi-type end users,combined with the analysis of comparative schemes,the validity of the proposed method based on big data mining is further verified. |
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