Power short-term load forecasting based on big data and optimization neural network

被引：0

作者：

Jin X. ^{[1
]}

Li L.-W. ^{[1
]}

Ji J.-N. ^{[2
]}

Li Z.-Q. ^{[3
]}

Hu Y. ^{[3
]}

Zhao Y.-B. ^{[4
]}

机构：

[1] School of Information, Central University of Finance and Economics, Beijing

[2] Personnel Testing Center, Ministry of Human Resources and Social Security, Beijing

[3] Beijing State Power Communication Network Technology Company, Beijing

[4] Liaoning Power Supply Company ICT Branch of State Grid Corporation, Shenyang

来源：

Tongxin Xuebao/Journal on Communications | 2016年 / 37卷

基金：

中国国家自然科学基金;

关键词：

Electric power data; Parallel PSO to optimize the neural network; Particle swarm algorithm; Power load factor; Power load forecasting;

D O I：

10.11959/j.issn.1000-436x.2016245

中图分类号：

学科分类号：

摘要：

With the reduction of the cost of power data acquisition and the interconnection of large scale power systems, the types of data available in the power network are becoming more and more abundant. In the past, the centralized forecasting method was limited to the analysis of the massive power data. Therefore, a short-term power load forecasting based on large data and particle swarm optimization BP neural network was proposed, and short-term power load forecasting model was established. The actual load data of the national grid, using the method of prediction, compared with the actual load data and centralized load forecasting results prove that this method is accurate enough, reduce the load forecasting time with feasibility in practical application. © 2016, Editorial Board of Journal on Communications. All right reserved.

引用

页码：36 / 42

页数：6

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