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风电场短期风速预测方法对比研究

发表时间:2011-03-31  浏览量:1835  下载量:876
全部作者: 龚松建,袁宇浩,王莉,张广明
作者单位: 南京工业大学自动化与电气工程学院
摘 要: 介绍3种风电场风速预测模型,分别为时间序列ARMA模型、BP神经网络模型和小波神经网络组合模型。时间序列ARMA模型和BP神经网络模型是风速预测中常用的模型,为了提高预测精度,引入了小波神经网络组合模型。对3种方法在短期风速预测中的应用进行研究和比较,并针对不同的风速样本进行分析,研究结果表明:在风速较平稳时,宜采用BP神经网络模型进行预测;在风速波动较大时,可以采用小波神经网络模型预测。实际运用时,应根据具体情况进行分析和判断,选择合适的模型,以取得最优预测结果。
关 键 词: 控制理论与控制工程;风速预测;时间序列;神经网络;小波分析
Title: Short-term wind speed forecasting method for wind farms
Author: GONG Songjian, YUAN Yuhao, WANG Li, ZHANG Guangming
Organization: Institute of Automation and Electrical Engineering, Nanjing University of Technology
Abstract: This paper describes three kinds of wind speed forecasting models, namely the time-series model, BP neural network model and wavelet neural network combined model. Time-series ARMA model and BP neural network model are commonly used models in the wind speed forecast, in order to improve prediction accuracy, wavelet neural network combined model has been taken into account. The three methods in the short term wind speed forecasting have been studied and compared, and wind speed from different samples has been analyzed, the results showed that: if the wind speed is relatively stable, it is desirable to use BP neural network model to predict; if the wind speed fluctuates too large, it is desirable to use wavelet neural network model in the prediction. In the actual application, it is proper to use the method which is suitable for the circumstance, to select the appropriate model in order to obtain the optimal prediction.
Key words: control theory and control engineering; wind speed forecasting; time series; neural network; wavelet analysis
发表期数: 2011年3月第6期
引用格式: 龚松建,袁宇浩,王莉,等. 风电场短期风速预测方法对比研究[J]. 中国科技论文在线精品论文,2011,4(6):496-501.
 
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