| 引用本文: | 王东风,王富强,牛成林.小波分解层数及其组合分量对短期风速多步预测的影响分析[J].电力系统保护与控制,2014,42(8):82-89.[点击复制] |
| WANG Dong-feng,WANG Fu-qiang,NIU Cheng-lin.Analysis of wavelet decomposition for multi-step prediction of short-term wind speed[J].Power System Protection and Control,2014,42(8):82-89[点击复制] |
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| 摘要: |
| 针对风速时间序列的规律性和随机性双重特征，将小波分解和RBF神经网络相结合用于短期风速预测。针对小波分解用于风速信号的不同频率成份的趋势项提取，研究了基于小波分解后的分量RBF网络预测及综合问题，包括全部高频-低频分量组合预测、部分高频-低频分量组合预测，以及低频分量组合预测三种方法的预测性能和特点。分析了三种不同方法在短期风速预测中的应用效果。通过对不同时间、不同地点短期风速预测的研究发现，进行不同步数的预测时，只有选取合适的分解层数、合适的高频分量和低频分量组合，才能得到最优的预测效果。该结论对于将小波分解用于短期风速时间序列的预测具有一定的指导意义。 |
| 关键词: 风速预测 小波分解 RBF网络 时间序列 多步预测 |
| DOI：10.7667/j.issn.1674-3415.2014.08.014 |
| 投稿时间：2013-07-25修订日期：2013-09-24 |
| 基金项目:高等学校博士学科点专项科研基金(20120036120013)；中央高校基本科研业务费(11MG49) |
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| Analysis of wavelet decomposition for multi-step prediction of short-term wind speed |
| WANG Dong-feng,WANG Fu-qiang,NIU Cheng-lin |
| (Department of Automation, North China Electric Power University, Baoding 071003, China;Shenhua Guohua (Beijing) Electric Power Research Institute Co., Ltd, Beijing 100069, China) |
| Abstract: |
| Aiming at the double characteristics of regularity and randomness of wind speed series, wavelet decomposition combined with radial basis function (RBF) neural network are used for short term prediction of wind speed. Aiming at the trend term extraction of different components with different frequencies in wavelet decomposition of wind speed signal, RBF network prediction for different components decomposed with wavelet and the corresponding synthesization method are studied, which includes three kinds of decomposition-combination prediction methods, i.e. prediction using all-high-frequency and low-frequency components, prediction using part-high-frequency and low-frequency components, and prediction using low-frequency component. The prediction performances and characteristics are analyzed. Prediction results, which are based on the data sampled from different dates and different sites, are analyzed in the short-term wind speed prediction by using different methods, and the conclusion is that the optimal prediction results can be obtained only when appropriate decomposition layers, appropriate combination of high-frequency and low-frequency components are used. The conclusions have profound guiding significance for wavelet decomposition-based short term prediction of wind speed. |
| Key words: wind speed prediction wavelet decomposition RBF neural network time series multi-step prediction |
