Neural Network Load Forecasting Model Based on BP

被引：0

作者：

Gao, Xi ^{[1
]}

机构：

[1] Sch North China Elect Power Univ, Baoding 071000, Peoples R China

来源：

PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MACHINERY, ELECTRONICS AND CONTROL SIMULATION (MECS 2017) | 2017年 / 138卷

关键词：

neural network model; nonlinear regression; Short-term load forecasting;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Short-term load forecasting is the basis of power system operation and analysis, which is of great significance for unit commitment, economic load dispatching, safety checking and so on. As a result, improving the accuracy of load prediction is an important way to ensure the scientific decision-making of power system optimization. In this paper, based on the annual load data in two regions, we predict the discrete data by the BP neural network load forecasting model, to get the load forecast data for a given period of time in the future. In the end, the advantages and disadvantages of the two regions are evaluated, and we can get the better prediction model.

引用

页码：221 / 225

页数：5

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