A Predictive Tool For Grid Data Analysis Using Machine Learning Algorithms

被引：0

作者：

Penn, David ^{[1
]}

Subburaj, Vinitha Hannah ^{[1
]}

Subburaj, Anitha Sarah ^{[1
]}

Harral, Mark ^{[2
]}

机构：

[1] West Texas A&M Univ, Sch Engn Comp Sci & Math, Canyon, TX 79016 USA

[2] Grp NIRE, Lubbock, TX USA

来源：

2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2020年

关键词：

Bigdata; Grid Data; Distributed Energy Resources (DER); Machine Learning Algorithms; ELECTRICITY PRICE; NEURAL-NETWORK;

D O I：

10.1109/ccwc47524.2020.9031265

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Power and energy sectors are collecting vast amount of data from different sources and trying to use computational tools to analyze and identify useful patterns in the data collected. Some of challenges observed with such big data are high volume, heterogeneous, and rapidly growing data. To efficiently handle such big data, machine learning algorithms are used. In this paper, such machine learning algorithms are used to predict patterns in the grid data collected from the Distributed Energy Resources (DER) at a local electrical engineering company. Predictive framework developed to preprocess the big data, classify the test and training datasets, and the application of different machine learning algorithms is discussed in this paper. The results obtained after analyzing the big data with different machine learning algorithms are also discussed in this paper.

引用

页码：1071 / 1077

页数：7

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