The multi-dimensional power big data mining based on improved grey clustering algorithm

Web Intell. Pub Date : 2022-11-08 DOI:10.3233/web-220048
Hui Li, Guangqian Lu
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Abstract

In order to overcome the problems of the traditional power big data mining methods, such as the low integrity of data mining and the long time-consuming of data mining, this paper realizes multi-dimensional power big data mining by improving the grey clustering algorithm. Firstly, a relay multi hop network is established to collect power big data through the collector; Secondly, Lagrange interpolation method is used to fill the missing data of power data mining; Standardized processing of power consumption data; Finally, according to the grey theory and FCM clustering algorithm, the multi-dimensional power big data mining is realized. The experimental results show that the integrity of power big data mining in this method is up to 0.996, the mining time is not more than 3.05 s, and the mining integrity is up to 0.992, which indicates that this method can effectively improve the effect of power big data mining.
基于改进灰色聚类算法的多维功率大数据挖掘
为了克服传统电力大数据挖掘方法存在的数据挖掘完整性低、耗时长等问题,本文通过改进灰色聚类算法实现了多维电力大数据挖掘。首先,建立中继多跳网络,通过采集器采集电力大数据;其次,采用拉格朗日插值法对幂数据挖掘的缺失数据进行填充;电耗数据的标准化处理;最后,根据灰色理论和FCM聚类算法,实现了多维电力大数据挖掘。实验结果表明,该方法电力大数据挖掘的完整性可达0.996,挖掘时间不超过3.05 s,挖掘完整性可达0.992,表明该方法可以有效提高电力大数据挖掘的效果。
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