杨玉强,胡若云
YANG Yuqiang,HU Ruoyun

• 1:国网浙江省电力有限公司

摘要(Abstract)：

随着电力市场化改革推进，电力改革出现市场主体多元化、利益诉求多样化的新趋势。为了解决电力市场售电均价水平偏高，部分用户用电成本上涨的问题，以售电公司售电均价异常智能分析为切入点，构建售电公司代理零售用户市场化均价异常分析模型；运用DBSCAN聚类分析算法定位电价异常的零售用户，智能划分不同异常用户群体并进行溯源分析；分析结果便于电网公司主动服务用户降低成本，同时辅助政府决策，推动电力市场化改革。
With the advancement of the market-oriented reform of electricity, a new trend of pluralistic market entities and diversified interest demands has emerged in the power reform. In order to solve the problem of a high average price level of electricity sold in the electricity market and the rising cost of electricity for some customers, the intelligent analysis of average electricity selling price anomaly of electricity selling companies is conducted first, and an analysis model for market-oriented average electricity price anomaly of retail users with electricity sales company as their agent is built. The DBSCAN clustering algorithm is used to locate retail users with electricity price anomaly, intelligently divide different groups of customers with the anomaly and conduct traceability analysis so that power grid enterprises can proactively serve customers, reduce costs, assist government decision-making, and promote market-oriented reform of electricity.

关键词(KeyWords)： 零售均价;DBSCAN算法;异常挖掘;聚类分析
average retail price;DBSCAN algorithm;anomaly extraction;cluster analysis

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 杨玉强,胡若云
YANG Yuqiang,HU Ruoyun

DOI: 10.19585/j.zjdl.202304009

参考文献(References)：

• [1]张友国.电力市场化改革对电价的影响[J].价格理论与实践，2006(10):39-40.ZHANG Youguo.Influence of electricity market reform on electricity price[J].Price:Theory&Practice,2006(10):39-40.
• [2]李景峰.电价立法问题研究[D].呼和浩特：内蒙古大学，2015.LI Jingfeng.Research on the legislation of electricity price[D].Hohhot:Inner Mongolia University,2015.
• [3]刘力昌，夏梦.国内电力定价机制改革研究与建议[J].开发研究，2015(1):133-136.LIU Lichang,XIA Meng.Research and suggestions on the reform of domestic electricity pricing mechanism[J]. Research on Development,2015(1):133-136.
• [4]田甜.电力价格政府管制研究：以湖南省为例[D].长沙：中南大学，2008.TIAN Tian. Research on government regulation of electricity price—taking Hunan Province as an example[D].Changsha:Central South University,2008.
• [5]王慧.销售电价“四分”管理方法研究[J].企业管理，2017（增刊2）:78-79.WANG Hui. Research on “four points” management method of sales electricity price[J]. Enterprise Management,2017(S2):78-79.
• [6]彭显刚，林利祥，刘艺，等.数据挖掘技术在电价执行稽查中的应用研究[J].电气应用，2016,35(11):62-67.PENG Xiangang,LIN Lixiang,LIU Yi,et al.Research on the application of data mining technology in electricity price execution inspection[J]. Electrotechnical Application,2016,35(11):62-67.
• [7]周水庚，周傲英，曹晶，等.一种基于密度的快速聚类算法[J].计算机研究与发展，2000,37(11):1287-1292.ZHOU Shuigeng,ZHOU Aoying,CAO Jing,et al.A fast density based clustering algorithm[J].Journal of Computer Research and Development,2000,37(11):1287-1292.
• [8]周水庚，周傲英，曹晶.基于数据分区的DBSCAN算法[J].计算机研究与发展，2000,37(10):1153-1159.ZHOU Shuigeng,ZHOU Aoying,CAO Jing. A datapartitioning-based dbscan algorithm[J]. Journal of Computer Research and Development,2000,37(10):1153-1159.
• [9]周水庚，范晔，周傲英.基于数据取样的DBSCAN算法[J].小型微型计算机系统，2000,21(12):1270-1274.ZHOU Shuigeng,FAN Ye,ZHOU Aoying.DBSCAN algorithm based on data sampling[J].Mini-Micro Systems,2000,21(12):1270-1274.
• [10]张建萍，刘希玉.基于聚类分析的K-means算法研究及应用[J].计算机应用研究，2007,24(5):166-168.ZHANG Jianping,LIU Xiyu.Research and application of K-means algorithm based on cluster analysis[J].Computer Application Research,2007(5):166-168.
• [11]吕巍，蒋波，陈洁.基于K-means算法的中国移动市场顾客行为细分策略研究[J].管理学报，2005,2(1):80-84.LüWei,JIANG Bo,CHEN Jie.Research of the segmentation strategy of customer behavior in Chinese mobile market based on the K-means arithmetic[J].Chinese Journal of Management,2005,2(1):80-84.
• [12]董秋仙，朱赞生.一种新的选取初始聚类中心的K-means算法[J].统计与决策，2020,36(16):32-35.DONG Qiuxian,ZHU Zansheng. A new K-means algorithm for selecting initial clustering center[J].Statistics&Decision,2020,36(16):32-35.
• [13]何振峰，熊范纶.结合限制的分隔模型及K-Means算法[J].软件学报，2005,16(5):799-809.HE Zhenfeng,XIONG Fanlun. A constrained partition model and K-means algorithm[J]. Journal of Software,2005,16(5):799-809.
• [14]张宁，贾自艳，史忠植.使用KNN算法的文本分类[J].计算机工程，2005,31(8):171-172.ZHANG Ning,JIA Ziyan,SHI Zhongzhi.Text categorization with KNN algorithm[J]. Computer Engineering,2005,31(8):171-172.
• [15]闭小梅，闭瑞华.KNN算法综述[J].科技创新导报，2009,6(14):31.BI Xiaomei,BI Ruihua.Summary of KNN algorithm[J].Science and Technology Innovation Herald,2009,6(14):31.
• [16]耿丽娟，李星毅.用于大数据分类的KNN算法研究[J].计算机应用研究，2014,31(5):1342-1344.GENG Lijuan,LI Xingyi. Improvements of KNN algorithm for big data classification[J]. Application Research of Computers,2014,31(5):1342-1344.
• [17]冯少荣，肖文俊.DBSCAN聚类算法的研究与改进[J].中国矿业大学学报，2008,37(1):105-111.FENG Shaorong,XIAO Wenjun.An improved DBSCAN clustering algorithm[J]. Journal of China University of Mining&Technology,2008,37(1):105-111.
• [18]赵卫中，马慧芳，李志清，等.一种结合主动学习的半监督文档聚类算法[J].软件学报，2012,23(6):1486-1499.ZHAO Weizhong,MA Huifang,LI Zhiqing,et al. Efficiently active learning for semi-supervised document clustering[J].Journal of Software,2012,23(6):1486-1499.
• [19] GENG Z S,YING Z A,WEN J,et al. FDBSCAN:A Fast DBSCAN Algorithm[J].Journal of Software,2000,15(6):735-744.
• [20]宋明，刘宗田.基于数据交叠分区的并行DBSCAN算法[J].计算机应用研究，2004,21(7):17-20.SONG Ming,LIU Zongtian. A data-overlap-partitioningbased parallel DBSCAN algorithm[J]. Application Research of Computers,2004,21(7):17-20.