Short-term Wind Power Integration Prediction Method Based on Error Correction

被引：0
|
作者：
Ding T. [1 ]
Yang M. [1 ]
Yu Y. [1 ]
Si Z. [1 ]
Zhang Q. [2 ]
机构：
[1] School of Electrical Engineering, Shandong University, Jinan
[2] Shandong Electric Power Dispatching Control Center, State Grid Shandong Electric Power Company, Jinan
来源：
Gaodianya Jishu/High Voltage Engineering | 2022年 / 48卷 / 02期
关键词：
Error correction; Improved particle swarm optimization; Integrated prediction; Random forest model; Wind power; XGBoost model;
D O I：
10.13336/j.1003-6520.hve.20201804
中图分类号：
学科分类号：
摘要：
To improve the accuracy of short-term wind power prediction, a short-term wind power integrated prediction model based on error correction is proposed. A wind power prediction model is established by using the XGBoost model based on im-proved particle swarm optimization firstly. Based on the relationship between wind speed and power, the prediction error of the XGBoost model is divided into low wind speed power error, middle-speed power error, and high wind speed power error. The random forest is trained under each type of error, then the corresponding power error prediction model is obtained. The short-term wind power prediction results based on error correction can be obtained by the addition of XGBoost model prediction results and power error prediction results. The research results show that the proposed model improves the short-term wind power prediction accuracy through integrated learning and residual learning methods, therefore, the proposed model can promote the wind power consumption capacity and improve the economy of the power system. © 2022, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：488 / 496
页数：8
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