基于大数据技术的电力用户需求挖掘方法

Power User Demand Mining Method Based on Big Data Technology

由于传统电力用户需求挖掘方法准确度低且计算过程复杂,本文探究了基于大数据技术的电力用户需求挖掘方法。根据外在因素分析用户行为特征,通过电器使用程度确定参考指标与用电量;利用K均值聚类算法处理行为数据,使用关联规则深度挖掘用户需求,实现利用大数据技术分析用户需求与习惯倾向。测试结果表明:使用大数据技术挖掘到的用户用电量与实际采集到的用电量之间差值不超过0.45 kW,可见本文方法可以达到预想效果。

Due to the low accuracy and complex calculation process of traditional power user demand mining methods,the power user demand mining method based on big data technology is studied.Analyze user behavior characteristics according to external factors,and determine reference indicators and power consumption through the degree of electrical appliance use.Use K-means clustering algorithm to process behavioral data,use association rules to deeply mine user needs,and use big data technology to analyze user needs and habits.The test results show that the difference between the user power consumption mined by big data technology and the actual collected power consumption is not more than 0.45 kW,which shows that this method can achieve the pre effect.