基于小波变换的风电场短期风速组合预测

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摘要针对风电场短期风速的预测提出一种基于小波变换的组合预测方法。首先利用Mallat算法对短期风速时间序列进行db3小波三层分解与重构,得到短期风速时间序列的近似分量和细节分量。针对近似分量和细节分量的不同特性,对近似分量利用粒子群算法优化的最小二乘支持向量机进行预测,对细节分量利用自回归求和滑动平均模型进行预测。最后各预测模型预测值组合叠加得到最终的短期风速预测值。仿真结果表明该方法具有较高的预测准确度。

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	田中大
	李树江
	王艳红
	高宪文

关键词 ：短期风速, 小波变换, 自回归求和滑动平均模型, 最小二乘支持向量机, 组合预测

Abstract：In order to improve short-term wind speed prediction accuracy of wind farms,a combined prediction method based on the wavelet transform is proposed.Firstly,the db3 wavelet is used for three-layer decomposition and reconstruction for short-term wind speed time series through Mallat algorithm.The approximation components and the detail components of the short-term wind speed are then obtained.Next,according to the different characteristics of these components,the least square support vector machine optimized by particle swarm algorithm and the autoregressive integrated moving average model are adopted as the predictive models for the approximate components and the detail components respectively.Then,the final predictive value of the short-term wind speed is obtained by the combination of the two components.The simulation results indicate that higher accuracy can be obtained in this prediction method.

Key words： Short-term wind speed wavelet transform auto regressive integrated moving average least square support vector machine combined prediction

收稿日期: 2014-12-04 出版日期: 2015-05-22

PACS:

TM614

基金资助:国家自然科学基金重点项目(61034005)和辽宁省博士科研启动基金(20141070)资助。

通讯作者: 田中大男,1978年生,博士,讲师,研究方向为混沌时间序列预测、网络控制系统时延补偿与调度方法。

作者简介: 田中大男,1978年生,博士,讲师,研究方向为混沌时间序列预测、网络控制系统时延补偿与调度方法。李树江男,1966年生,博士,教授,研究方向为复杂工业过程建模与优化控制。

引用本文:

田中大, 李树江, 王艳红, 高宪文. 基于小波变换的风电场短期风速组合预测[J]. 电工技术学报, 2015, 30(9): 112-120. Tian Zhongda , Li Shujiang , Wang Yanhong , Gao Xianwen. Short-Term Wind Speed Combined Prediction for Wind Farms Based on Wavelet Transform. Transactions of China Electrotechnical Society, 2015, 30(9): 112-120.

链接本文:

https://dgjsxb.ces-transaction.com/CN/Y2015/V30/I9/112

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