基于卡尔曼滤波的风速序列短期预测方法

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摘要分析了卡尔曼滤波在风速序列预测分析中的应用机理,构造了用于风速序列预测分析的迟滞神经网络,并采用卡尔曼滤波方法将其与ARMA模型相融合,实现了风速序列的混合预测。通过修改激励函数的方式将迟滞特性引入神经网络,网络的权值采用梯度寻优的方式确定,迟滞参数利用遗传算法进行确定。系统的状态方程采用ARMA模型建立,将迟滞神经网络对风速序列的预测结果作为测量方程的测量值。混合预测方法能减小单一预测机制造成的同一性质误差的累积。仿真实验结果表明,迟滞神经网络的预测性能优于传统BP神经网络,而混合预测方法的预测性能优于单一预测方法。

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	修春波
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	刘明凤

关键词 ：卡尔曼滤波, 风速序列, 神经网络, ARMA模型, 预测

Abstract：The prediction mechanism of Kalman filtering for wind speed series is analyzed. And a hysteretic neural network is proposed to predict the wind speed series. The hybrid prediction of wind speed series, combining ARMA model and hysteretic neural network, based on Kalman filtering fusion is completed. Hysteretic property is brought into neural network by changing the convention activation function to hysteretic activation function. The connective weights of network are determined by gradient optimization, and hysteretic parameters are determined by genetic algorithm. State equation is established by ARMA model. The prediction results of hysteretic neural network are taken as measurement values. Hybrid prediction can prevent error accumulation caused by the single prediction mechanism. Simulation results show that the hysteretic neural network has better prediction performance than BP neural network, and the hybrid prediction is superior to each single method.

Key words： Kalman filtering wind speed series neural network ARMA model prediction

收稿日期: 2013-07-17 出版日期: 2014-06-18

PACS:

TK89

基金资助:国家自然科学基金资助项目(61203302)

作者简介: 修春波男,1978年生,副教授,博士,研究方向为风能利用及风速预测。任晓男,1990年生,硕士研究生,研究方向为时间序列预测分析。

引用本文:

修春波, 任晓, 李艳晴, 刘明凤. 基于卡尔曼滤波的风速序列短期预测方法[J]. 电工技术学报, 2014, 29(2): 253-259. Xiu Chunbo, Ren Xiao, Li Yanqing, Liu Mingfeng. Short-Term Prediction Method of Wind Speed Series Based on <br/>Kalman Filtering Fusion. Transactions of China Electrotechnical Society, 2014, 29(2): 253-259.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2014/V29/I2/253

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