[1]黄文聪,张宇,杨远程,等.模糊信息粒化和GWO-SVM算法结合的短期风速范围预测[J].华侨大学学报(自然科学版),2020,41(5):674-682.[doi:10.11830/ISSN.1000-5013.201911024]
　HUANG Wencong,ZHANG Yu,YANG Yuancheng,et al.Short-Term Wind Speed Range Prediction Based on Fuzzy Information Granulation and GWO-SVM Algorithm[J].Journal of Huaqiao University(Natural Science),2020,41(5):674-682.[doi:10.11830/ISSN.1000-5013.201911024]
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模糊信息粒化和GWO-SVM算法结合的短期风速范围预测()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第41卷
期数:
2020年第5期
页码:
674-682
栏目:
出版日期:
2020-09-20

文章信息/Info

Title:
Short-Term Wind Speed Range Prediction Based on Fuzzy Information Granulation and GWO-SVM Algorithm
文章编号:
1000-5013(2020)05-0674-09
作者:
黄文聪;  张宇;  杨远程;  李子修;  陈润;  常雨芳
湖北工业大学 太阳能高效利用及储能运行控制湖北省重点实验室, 湖北 武汉 430068
Author(s):
HUANG Wencong;  ZHANG Yu;  YANG Yuancheng;  LI Zixiu;  CHEN Run;  CHANG Yufang
Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
关键词:
风速预测;  时间序列;  模糊信息粒化;  灰狼算法;  支持向量机
Keywords:
wind speed prediction;  time series;  fuzzy information granulation;  grey wolf optimizer;  support vector machine
分类号:
TM614;TP301.6
DOI:
10.11830/ISSN.1000-5013.201911024
文献标志码:
A
摘要:
为了实现对风速范围区间的准确预测,提出一种基于模糊信息粒化和灰狼优化-支持向量机(GWO-SVM)算法的风速预测模型.该模型首先利用模糊信息粒子,从一段连续时间的风速值提取出最大值、最小值及大致的平均水平值;然后,采用时间序列风速输入模型,构建输入支持向量机模型的标签向量与特征矩阵;最后,通过灰狼算法进行支持向量机预测模型的参数寻优,实现对风速范围区间的准确预测.在实例验证阶段,将风速历史数据进行模糊粒化,采取4种不同的参数寻优方式对支持向量机预测模型进行参数寻优.结果表明:GWO-SVM算法可以有效地提高风速范围预测的精确度.
Abstract:
In order to achieve accurate prediction of wind speed range interval, this paper proposes a wind speed prediction model based on fuzzy information granulation and grey wolf optimizer-support vector machine(GWO-SVM)algorithm. Firstly, the fuzzy information granules are used to extract the maximum, minimum and general average values of wind speed for a continuous period of time. Then the label vector and feature matrix are constructed as input of the SVM model by the time series wind speed prediction model, and finally the parameters of the SVM prediction model are optimized by the gray wolf algorithm to realize the accurate prediction of the wind speed range interval. On the example verified stage, the fuzzy information granulation of historical wind speed data is carried out, and the parameters of the SVM prediction model are optimized by four different parameter optimization methods. The results show that the GWO-SVM algorithm can improve the accuracy of wind speed range interval prediction more effectively.

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备注/Memo

备注/Memo:
收稿日期: 2019-11-06
通信作者: 黄文聪(1977-),男,副教授,博士,主要从事能源系统优化与控制、电力系统规划的研究.E-mail:hwc@hbut.edu.cn.
基金项目: 国家自然科学基金资助项目(61903129, 51977061); 湖北工业大学绿色工业引领计划资助项目(CPYF2017003); 湖北工业大学大学生创新创业训练计划资助项目(201710500002, S201810500045, S2019105000
更新日期/Last Update: 2020-09-20