Wind Power Prediction Method Using Hybrid Kernel LSSVM With Batch Feature

被引：0

作者：

Liu C. ^{[1
,2
]}

Lang J. ^{[3
,4
]}

机构：

[1] Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Ministry of Education, Shenyang

[2] Institute of Industrial and Systems Engineering, Northeastern University, Shenyang

[3] Liaoning Key Laboratory of Manufacturing System and Logistics, Northeastern University, Shenyang

[4] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang

来源：

Liu, Chang (lc1987328@126.com) | 1600年 / Science Press卷 / 46期

基金：

中国国家自然科学基金;

关键词：

Batch feature; Differential evolution (DE); Hybrid kernel least squares support vector machine (HKLSSVM); Sparse selection; Wind power prediction;

D O I：

10.16383/j.aas.c180103

中图分类号：

学科分类号：

摘要：

For the wind power prediction problem in a wind farm, this paper collects some related data such as historical wind power data, meteorological data, and wind speed data sampled by anemometer tower. Then, a wind power prediction method with batch feature is proposed, which is based on hybrid kernel least squares support vector machine (HKLSSVM). It is used to establish the wind power prediction model in the wind farm. To enhance the model's adaptability, an improved differential evolution algorithm is designed to optimize the model parameters, and a sparse selection method is used to select the appropriate training samples set. Thus, the modeling time is shortened and the prediction model accuracy is guaranteed. According to the location distribution of wind turbines in the wind farm, a modeling strategy based on batch partition is proposed, some wind turbines at similar locations can be clustered by batch strategy, which is used instead of the traditional wind power prediction methods in the wind farm. The proposed model is tested through the real data in the wind farm. Experimental results show that the proposed method can improve the accuracy and efficiency of wind power prediction compared with other prediction methods, and can reduce the effect of the wind fluctuation. Hence it can ensure the safety and reliability of the power grid. Copyright © 2020 Acta Automatica Sinica. All rights reserved.

引用

页码：1264 / 1273

页数：9

共 29 条

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