[1]王腾洲,李森文,黄宇轩,等.基于 IWOA-SVM 的风电功率预测[J].机械与电子,2022,(05):9-12.
 WANG Tengzhou,LI Senwen,HUANG Yuxuan,et al.Wind Power Forecast Based on IWOA-SVM[J].Machinery & Electronics,2022,(05):9-12.
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基于 IWOA-SVM 的风电功率预测()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2022年05期
页码:
9-12
栏目:
设计与研究
出版日期:
2022-05-24

文章信息/Info

Title:
Wind Power Forecast Based on IWOA-SVM
文章编号:
1001-2257 ( 2022 ) 05-0009-04
作者:
王腾洲 1 李森文 1 黄宇轩 2 郝思鹏 1
1. 南京工程学院电力工程学院,江苏 南京 211167 ; 2. 电子科技大学信息与通信工程学院,四川 成都 611731
Author(s):
WANG Tengzhou1 LI Senwen1 HUANG Yuxuan2 HAO Sipeng1
(1.School of Electric Power Engineering , Nanjing Institute of Technology , Nanjing 211167 , China ; 2.School of Information and Communication Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China )
关键词:
风电功率预测支持向量机鲸鱼优化算法 Tent 混沌映射轮盘赌选择法
Keywords:
wind power prediction support vector machine whale optimization?algorithm Tent chaotic map roulette selection method
分类号:
TM614
文献标志码:
A
摘要:
针对支持向量机预测精度低、收敛速度慢等问题,提出一种改进鲸鱼算法优化支持向量机的风电功率预测模型。将 Tent 混沌映射引入鲸鱼算法中,使初始种群的分布更加均匀;由于随机抽取猎物具有盲目性,不能充分结合迭代经验对种群进行更新,采用轮盘赌法寻找目标猎物来加快鲸鱼算法的收敛速度,得到改进鲸鱼算法优化支持向量机的风电功率预测模型。将该模型应用到我国东北某处风电场进行风电功率预测,并与其他常用的功率预测模型进行对比分析,仿真结果表明,该模型具有更高的预测精度。
Abstract:
Aiming at the problems of low prediction accuracy and slow convergence speed of support vector machine , a wind power prediction model based on improved whale algorithm and optimization support vector machine is proposed.The Tent chaotic map is introduced into the whale algorithm to make the distribution of the initial population more uniform ; due to the blindness of randomly extracting prey , the population cannot be fully updated with the iterative experience.The roulette method is used to find the target prey to speed up the convergence speed of the whale algorithm.The wind power prediction model based on the improved whale algorithm optimization support vector machine is obtained.The model is applied to a wind farm in Northeast China to predict wind power , and the results are compared with other commonly used power prediction models.The simulation results show that the model proposed in this paper has higher prediction accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2021-11-27
基金项目:国家自然科学基金面上项目( 51877101 )
作者简介:王腾洲 ( 1998- ),男,江苏南京人,硕士研究生,研究方向为风电并网特性分析;李森文 ( 1997- ),男,江苏南京人,硕士研究生,研究方向为新能源功率预测;黄宇轩 ( 1998- ),男,江苏泰兴人,硕士研究生,研究方向为雷达信号处理集群目标检测;郝思鹏 ( 1971- ),男,江苏宝应人,博士,教授,研究方向为配网自动化.
更新日期/Last Update: 2022-05-25