基于卷积神经网络与多维度电力大数据的信用评估系统

林晓静; 刘文; 甘超飞; 简燕红; 肖郑海

林晓静,刘文,甘超飞,简燕红,肖郑海.基于卷积神经网络与多维度电力大数据的信用评估系统[J].电测与仪表,2021,58(11):101-106.
Lin Xiaojing,Liu Wen,Gan Chaofei,Jian Yanhong,Xiao Zhenhai.Analysis of corporate credit portrait based on convolutional neural network and multi-dimensional power big data[J].Electrical Measurement & Instrumentation,2021,58(11):101-106.

基于卷积神经网络与多维度电力大数据的信用评估系统

Analysis of corporate credit portrait based on convolutional neural network and multi-dimensional power big data

DOI：10.19753/j.issn1001-1390.2021.11.014

中文关键词: 电力大数据卷积神经网络模糊聚类风险评估

英文关键词:power big data convolutional neural network fuzzy clustering corporation credit assessment.

基金项目:

作者中文名	作者英文名	单位
林晓静	Lin Xiaojing	国家电网有限公司大数据中心
刘文	Liu Wen	国家电网有限公司大数据中心
甘超飞	Gan Chaofei	北京中电普华信息技术有限公司
简燕红	Jian Yanhong	国家电网有限公司大数据中心
肖郑海	Xiao Zhenhai	国网信通亿力科技有限责任公司

摘要点击次数: 1606

中文摘要:

2017年,国家发展和改革委员会牵头启动“信易+”联盟建设,社会信用体系建设全面铺开。其中,企业信用评估是国家信用体系建设重要组成部分,国家对企业信用建设给予了高度重视。通过企业在电力使用上的信用评估系统构建,可以高效支撑电力行业对企业异常用电行为进行预警。在国家大力倡导社会信用体系建设和国家电网公司建设“三型两网、世界一流”企业的双重背景下,国家电网各业务部门及单位积极开展了自身信用体系建设。文中使用电力行业上下游的企业用户用电信息、履约情况、安全监管等数据,结合改进的模糊聚类算法对电力企业用户进行划分和用电信用系统构建,同时使用卷积神经网络算法(CNN)对企业信用评估系统进行分析建模。改进的模糊聚类算法能够适应不同的数据分布和提高聚类效果；改进后的多尺度卷积核CNN模型设计能克服传统CNN算法计算量大、易过拟合的缺点。实验证明多维度电力数据集可以很好反映企业信用信息,文中所提出的分类模型的运算效率和准确率较高,整体实现了企业电力风险评估系统。

英文摘要:

In 2017, the National Development and Reform Commission took the lead in launching the construction of the "Xinyi +" alliance, and the construction of the social credit system was fully rolled out. Among them, corporate credit assessment is an essential part, and the state attaches great importance to the construction of corporate credit. The construction of corporates’ credit assessment system on power use can effectively support the power industry in early warning of abnormal power consumption behaviors. Under the dual background of the state"s vigorous promotion of the construction of a social credit system and the construction of a "three-type, two-network, world-class" enterprise by the State Grid Corporation of China, various business departments and units of the State Grid Corporation of China have actively developed their credit system. This paper uses the power industry upstream and downstream customers" electricity consumption information, compliance status, safety supervision, and other data, combined with the improved fuzzy clustering algorithm to classify the power corporate users and construct group portraits. This paperalso utilizes the convolutional neural network (CNN) model and analyzes corporate electricity rise. The improved fuzzy clustering algorithm can adapt to different data distribution and model the corporate credit assessment system. The design of the multi-scale convolution kernel CNN model can overcome the shortcomings of the traditional CNN algorithm, such as a large calculation amount and easy to overfit. Experiments show that multi-dimensional power data sets can reflect corporate credit information well, and the proposed classification model has high computing efficiency and accuracy, which help to realize the Corporate Electricity Risk Assessment system.

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