Short Term Power load Forecasting Considering Meteorological Factors

被引：0

作者：

Luo, Jing ^{[1
]}

机构：

[1] North China Elect Power Univ, Dept Econ & Management, Baoding, Hebei, 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：

Short-term power load forecasting; Meteorological factors; Principal component regression analysis;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

To forecast short-term power load, we first establish GM (1, 1) grey forecasting model and test the correlation of the predicted values. Considering the impact of meteorological factors on modern power system, we establish a load forecasting model based on principal component analysis and multiple linear regression analysis. Then we compare the two kinds of load forecasting model by the precision of curve fitting with the actual load. The results show that the accurate of short-term load forecasting model included in meteorological factors is higher, we also introduce an assessment standard to provide evidence.

引用

页码：148 / 152

页数：5

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