基于BLP-ALO-SVM的风电功率短期预测方法

doi:10.16182/j.issn1004731x.joss.22-FZ0924

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (12): 2535-2545.doi: 10.16182/j.issn1004731x.joss.22-FZ0924

基于BLP-ALO-SVM的风电功率短期预测方法

焦业峰(), 王艳(), 纪志成

江南大学教育部物联网技术应用工程中心，江苏无锡 214122

收稿日期:2022-08-09 修回日期:2022-10-17 出版日期:2022-12-31 发布日期:2022-12-21
通讯作者: 王艳 E-mail:1226410700@qq.com;wangyan@jiangnan.edu.cn
作者简介:焦业峰(1996-)，男，硕士生，研究方向为新能源技术。E-mail：1226410700@qq.com
基金资助:
国家自然科学基金(61973138);国家重点研发计划(2018YFB1701903)

Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM

Yefeng Jiao(), Yan Wang(), Zhicheng Ji

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2022-08-09 Revised:2022-10-17 Online:2022-12-31 Published:2022-12-21
Contact: Yan Wang E-mail:1226410700@qq.com;wangyan@jiangnan.edu.cn

摘要/Abstract

摘要：

为了对短期风电功率及其波动范围作出有效预测，提出一种基于混合算法优化支持向量机的风电功率短期预测方法。采用探索性数据分析对原始风速数据进行预处理，提高风速数据的质量；采用混沌映射、莱维飞行策略和粒子群算法提升蚁狮算法的性能；应用经混合算法优化的支持向量机模型对风电功率进行预测。实验结果表明：相较于新型的风电功率预测模型，该方法输出的结果预测误差更低，表现出对于风电功率更好的预测能力。

关键词: 风电功率预测, 混合算法, 蚁狮算法, 莱维飞行策略, 混沌映射

Abstract:

To effectively predict the short-term wind power and its fluctuation range, a prediction method based on hybrid algorithm-optimized support vector machine is proposed. Exploratory data analysis is used to preprocess the original wind speed data to improve the data quality. Chaotic map, Levy flight strategy and particle swarm optimization are used to improve the ant lion algorithm. The support vector machine model optimized by hybrid algorithm is used to predict the wind power. The experimental results show that, compared with the new wind power prediction model, the prediction error of the output results of the method is lower, and the wind power. prediction ability is better.

Key words: wind power prediction, combination prediction, ant lion algorithm, levy flight, chaotic map

中图分类号:

TP391.9

焦业峰, 王艳, 纪志成. 基于BLP-ALO-SVM的风电功率短期预测方法[J]. 系统仿真学报, 2022, 34(12): 2535-2545.

Yefeng Jiao, Yan Wang, Zhicheng Ji. Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM[J]. Journal of System Simulation, 2022, 34(12): 2535-2545.

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模型	收敛次数	适应度值
ALO-SVM	51	0.683 9
LP- ALO-SVM	50	0.453 7
BP-ALO-SVM	49	0.594 8
BL-ALO-SVM	51	0.483 2
BLP-ALO-SVM	47	0.285 4

模型	收敛次数	适应度值
WHO-SVM	50	0.893 8
ALO-SVM	51	0.683 9
BLP-ALO-SVM	48	0.337 1

模型	MAPE/%	MAE	RSME
WHO-SVM	1.488 3	0.523 1	0.615 0
ALO-SVM	1.328 9	0.483 3	0.564 0
BLP-ALO-SVM	0.871 4	0.315 9	0.394 1

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基于BLP-ALO-SVM的风电功率短期预测方法

Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM

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