摘要
基于小波的多分辨率分析,针对风速序列拟周期性、非平稳性及非线性等特点,将风速序列按不同频率进行分解,对分解后的原始风速信号分别建立不同的预测模型;各个模型的最佳参数由贝叶斯证据3层推断得出,用以建立基于小波和贝叶斯证据推断框架下的最小二乘支持向量机(LS-SVM)回归短期风速预测模型。应用该模型对东北某风电场的风速进行了提前1 h的预测,预测的平均绝对百分比误差为7.63%,提高了预测精度。预测结果表明:基于贝叶斯证据推断框架下的LS-SVM和小波分析相结合的短期风速预测模型是一种有效、可行的风速预测模型,可为风力发电功率的预测提供一定的理论支持。
In view of the quasi-periodic, non-stationary and non-linear features of wind speed, the original wind speed sequence is decomposed into a series of sub-sequences based on the multiresolution analysis feature of wavelet. For each of these sub-sequences, a different tbrecasting model is established. The optimal parameters of every model can be found through the three-layer Bayesian evidence inference and they are used to establish the least squares support vector machine （LS-SVM） short-term wind speed forecasting model based on the wavelet decomposition and the Bayesian evidence inference framework, When the proposed method was applied in the one-hour-ahead wind speed prediction in a wind farm in the northeast region, the mean average percentage error of the predicted wind speed was only 7.63%, a large improvement of the prediction precision. The results verify the effectiveness of the proposed method.
出处
《能源技术经济》
2012年第5期31-35,共5页
Electric Power Technologic Economics
基金
福建省高等学校新世纪优秀人才支持计划项目(闽教科2010-24)~~
关键词
贝叶斯证据推断框架
最小二乘支持向量机
风速预测
小波分解
Bayesian evidence inference framework
least squares support vector machine （LS-SVM）
wind speed forecasting
wavelet decomposition （WD）
