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计算机系统应用:2019,28(3):104-110
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基于改进随机森林算法的停电敏感用户分类
(1.国网信通亿力科技有限责任公司, 福州 350001;2.国网福建省电力有限公司 电力科学研究院客户服务中心, 福州 350003;3.福州大学 数学与计算机科学学院, 福州 350116;4.福建省网络计算与智能信息处理重点实验室, 福州 350116)
Power Outage Sensitive Customers Classification Based on Improved Random Forest Algorithm
(1.State Grid Info-Telecom Great Power Science and Technology Co. Ltd, Fuzhou 350001, China;2.Customer Service Center, Electric Power Research Institute, State Grid Fujian Electric Power Co. Ltd., Fuzhou 350003, China;3.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;4.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou 350116, China)
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投稿时间:2018-08-20    修订日期:2018-09-18
中文摘要: 目前,我国电网企业对于识别停电投诉风险,开展用户停电敏感程度分析的研究工作还处在起步阶段.为了有效地分析停电用户的敏感程度,提出了一种基于改进随机森林算法的停电敏感用户分类算法.首先,对原始数据进行清洗、特征选择等预处理;接着,采用SMOTE算法增加少数敏感用户样本数据量,解决数据分布不均匀问题;然后,以Fisher比作为特征的重要性度量,按比例随机采样选取具有代表性的特征构成子特征空间;最后,利用随机森林算法识别停电敏感用户.通过在真实停电数据上的实验,验证了提出的方法不仅具有较好的准确性和时间性能,而且可以有效处理高维、冗余特征的数据.
Abstract:At present, the research on the risk identification of power outage complaints and the customer sensitivity analysis in power grid companies is at its early stage. In order to effectively analyze the sensitivity of power outage customers, a sensitive customer classification algorithm based on the improved random forest algorithm is proposed. First, the data is preprocessed by methods of data cleaning, feature selection, and so on. Second, the SMOTE algorithm is used to increase the number of sensitive customers to solve the problem of data imbalance. Third, the representative feature space is selected by proportional random sampling. The Fisher ratio is used as the characteristic importance measure. Then, the random forest algorithm is used to recognize the customers that are sensitive to power outage. Finally, the experiments on real power outage data show that the proposed method not only has better accuracy and time performance but also can effectively deal with high-dimensional data with redundant features.
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基金项目:国家自然科学基金(61300104,61300103,61672158);福建省高校杰出青年科学基金(JA12016);福建省高等学校新世纪优秀人才支持计划(JA13021);福建省杰出青年科学基金(2014J06017,2015J06014);福建省科技创新平台计划(2009J1007,2014H2005);福建省自然科学基金(2013J01230,2014J01232);福建省高校产学合作项目(2014H6014,2017H6008);海西政务大数据应用协同创新中心
引用文本:
谢国荣,郑宏,林伟圻,徐鸣,郭昆,陈基杰.基于改进随机森林算法的停电敏感用户分类.计算机系统应用,2019,28(3):104-110
XIE Guo-Rong,ZHENG Hong,LIN Wei-Qi,XU Ming,GUO Kun,CHEN Ji-Jie.Power Outage Sensitive Customers Classification Based on Improved Random Forest Algorithm.COMPUTER SYSTEMS APPLICATIONS,2019,28(3):104-110

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