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

Author:

Li Hui1,Lu Guangqian1

Affiliation:

1. Information center of Yunnan Power Grid Corporation, Kunming, 550002, Yunnan, China

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.

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Networks and Communications,Software

Reference16 articles.

1. Power equipment operation and maintenance and decision analysis method based on data mining;Cai;Journal of South China University of Technology (Natural Science),2019

2. Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

3. Distribution network project cost control method based on data mining;Dong;Computer and Network,2019

4. Research on power grid big data intelligent mining technology based on web crawler;Feng;Electronic Design Engineering,2019

5. Flower power: Finding optimal plant cutting strategies through a combination of optimization and data mining;Hoogeveen;Computers & Industrial Engineering,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3