Research and Application of Short-term Power Load Based on Large Data Analysis

被引：0

作者：

Ma, Zhi-cheng ^{[1
]}

Yang, Peng ^{[1
]}

Zhang, Lei ^{[1
]}

Zhao, Qiang ^{[2
]}

Zhang, Wen-qiang ^{[2
]}

机构：

[1] State Grid Power Co, Informat & Commun Co, Lanzhou, Gansu, Peoples R China

[2] North China Elect Power Univ, Beijing, Peoples R China

来源：

2016 INTERNATIONAL CONFERENCE ON POWER, ENERGY ENGINEERING AND MANAGEMENT (PEEM 2016) | 2016年

关键词：

Hadoop; SVM; Spark;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

We want to carry out an effectively distributed method to process large power data. Large data of electricity is first classified by big industries, and then by small industries, and finally put to use clustering to distinguish electro-sort of customers. This paper employed a computational framework named Spark based memory computing to conduct distributed computing, and adopted supporting vector machine on the Spark's, lastly the results were analyzed. Ensuring the final effect of the test, solution speed is greatly improved.

引用

页码：69 / 72

页数：4

共 4 条

[1] [Anonymous], 2013, INFORM TECHNOLOGY SP
[2] A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings
Hernandez, Luis
Baladron, Carlos
Aguiar, Javier M.
Carro, Belen
Sanchez-Esguevillas, Antonio J.
Lloret, Jaime
Massana, Joaquim
[J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2014, 16 (03): : 1460 - 1495
[3] Meina Song, 2013, SEVERAL KEY TECHNOLO
[4] Wang Shan, 2011, Chinese Journal of Computers, V34, P1741, DOI 10.3724/SP.J.1016.2011.01741

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