Load Forecasting Research Based on High Performance Intelligent Data Processing of Power Big Data

被引：2

作者：

Xu, Menghan ^{[1
]}

Huang, Gaopan ^{[1
]}

Zhang, Mingming ^{[1
]}

Cui, Peng ^{[1
]}

Wang, Chong ^{[1
]}

机构：

[1] State Grid Jiangsu Elect Power Co Ltd, Big Data Lab, Informat & Telecommun Branch, Nanjing 210024, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF THE 2018 2ND INTERNATIONAL CONFERENCE ON ALGORITHMS, COMPUTING AND SYSTEMS (ICACS 2018) | 2018年

关键词：

Power big data; load forecasting; power grid; chaos genetic algorithm (CGA);

D O I：

10.1145/3242840.3242842

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The method proposed in this paper is a data analysis method that intelligently analyzes power big data and realizes the load forecasting of power grid. The method calls for the corresponding data from each database of big data platform by accepting the load forecast request from the client, and performs the load forecasting in the big data by improving the gray model of chaos genetic algorithm (CGA). After the completion of load forecasting, the final output to the client load forecasting results.

引用

页码：55 / 60

页数：6

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