Two-stage Photovoltaic Power Forecasting and Error Correction Method Based on Statistical Characteristics of Data

被引：0

作者：

Liu J. ^{[1
]}

Chen X. ^{[1
]}

Lu C. ^{[1
]}

Mao H. ^{[2
]}

机构：

[1] Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing

[2] State Grid Zhejiang Electric Power Research Institute, Hangzhou, 310014, Zhejiang Province

来源：

Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 08期

关键词：

Cluster analysis; Error correction; Multiple linear regression; Photovoltaic power prediction; Principal component analysis;

D O I：

10.13335/j.1000-3673.pst.2020.0027a

中图分类号：

学科分类号：

摘要：

Accurate photovoltaic power (PV) prediction contributes to the efficient power grid dispatching and the reliable operation. In this paper, a two-stage PV prediction model with error corrections based on the statistical analysis is presented. Principal component analysis (PCA) is adopted to remove the collinearity of various meteorological values. Through fluctuation and cluster analysis, a novel model is proposed with more precise fittings to the output power and the meteorological types. Then, a preliminary prediction on PV power can be obtained by multiple linear regression models corresponding to each subset. To correct the errors further, the distribution characteristics of these errors are also analyzed. The validations based on practical PV power and meteorological data show the advantages of the proposed forecasting method. © 2020, Power System Technology Press. All right reserved.

引用

页码：2891 / 2897

页数：6

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