摘要
为了利用海量的电网客户数据对用电客户的用电行为进行分析和预测,在大数据的背景下,引入数据挖掘技术,设计了电网客户用电行为分析系统。根据客户的用电数据建立了用电客户细分及客户信用等级的分类标准。在数据挖掘方面,通过K-means算法对用电客户进行客户细分,利用ID3决策树算法对客户的信用度进行分类。最后以某电力客服中心具体数据进行测试,结果有效的反应各分类下的客户用电行为,系统满足设计要求。
In order to make use of the vast amounts of customer data of power grid for electricity customers with electrical behavior analysis and forecast, under the background of big data, the introduction of data mining technology, design the grid customer behavior analysis system. According to the customer data established by electric customer segmentation and customer credit rating classification standard. In terms of data mining, K-means algorithm is adopted for electricity customers for customer segmentation, using ID3 decision tree algorithm for classification of customer credit degree. And finally to a electric power customer service center specific data to test. The results effective response to the classification of customers conduct electricity system satisfies the design requirements.
出处
《电子设计工程》
2016年第17期61-63,69,共4页
Electronic Design Engineering
基金
国家自然科学基金(51190103)
关键词
大数据
数据挖掘
电网
用户行为分析
big data
data mining
power grid
user behavior analysis
