电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2014年
8期
82-89
,共8页
风速预测%小波分解%RBF网络%时间序列%多步预测
風速預測%小波分解%RBF網絡%時間序列%多步預測
풍속예측%소파분해%RBF망락%시간서렬%다보예측
wind speed prediction%wavelet decomposition%RBF neural network%time series%multi-step prediction
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。
針對風速時間序列的規律性和隨機性雙重特徵，將小波分解和RBF神經網絡相結閤用于短期風速預測。針對小波分解用于風速信號的不同頻率成份的趨勢項提取，研究瞭基于小波分解後的分量RBF網絡預測及綜閤問題，包括全部高頻-低頻分量組閤預測、部分高頻-低頻分量組閤預測，以及低頻分量組閤預測三種方法的預測性能和特點。分析瞭三種不同方法在短期風速預測中的應用效果。通過對不同時間、不同地點短期風速預測的研究髮現，進行不同步數的預測時，隻有選取閤適的分解層數、閤適的高頻分量和低頻分量組閤，纔能得到最優的預測效果。該結論對于將小波分解用于短期風速時間序列的預測具有一定的指導意義。
침대풍속시간서렬적규률성화수궤성쌍중특정，장소파분해화RBF신경망락상결합용우단기풍속예측。침대소파분해용우풍속신호적불동빈솔성빈적추세항제취，연구료기우소파분해후적분량RBF망락예측급종합문제，포괄전부고빈-저빈분량조합예측、부분고빈-저빈분량조합예측，이급저빈분량조합예측삼충방법적예측성능화특점。분석료삼충불동방법재단기풍속예측중적응용효과。통과대불동시간、불동지점단기풍속예측적연구발현，진행불동보수적예측시，지유선취합괄적분해층수、합괄적고빈분량화저빈분량조합，재능득도최우적예측효과。해결론대우장소파분해용우단기풍속시간서렬적예측구유일정적지도의의。
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.
