Short-Term Wind Power Prediction Based on Dynamic STARMA Model

被引：0

作者：

Liu, Yi ^{[1
,2
]}

Che, Ping ^{[1
]}

机构：

[1] Northeastern Univ, Dept Math, Shenyang 110819, Peoples R China

[2] Northeastern Univ, Inst Ind & Syst Engn, Shenyang 110819, Peoples R China

来源：

PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019) | 2019年

关键词：

Wind Power Prediction; ARIMA Model; Adjacency Matrix; Dynamic STARMA Model; SPEED;

D O I：

10.1109/ccdc.2019.8832755

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Wind power prediction plays an important role in the safe integration of wind power. In this paper, we propose a dynamic spatial-temporal autoregressive and moving average (STARMA) model for short-term wind power prediction. The adjacency matrix is determined based on the variations of wind speed and direction, and predicted by using the autoregressive integrated moving average (ARIMA) model. The proposed wind power prediction method can reduce the influence of wind direction on prediction accuracy. Experimental results demonstrate the effectiveness of the proposed prediction method.

引用

页码：5549 / 5554

页数：6

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