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基于小波变换和时间序列法考虑随机分量的短期风速预测 预览

Short-term Wind Speed Prediction Based on Wavelet Transform and Time Series Method and Random Components
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摘要 为提高短期风速预测的准确性,本文结合小波变换和时间序列法,考虑随机分量进行短期风速预测.利用小波变换对风速时间序列分层,将高频变化的风速时间序列利用自回归滑动平均模型(ARMA)进行预测,低频变化的风速时间序列利用持续法进行预测,最后将预测结果叠加并通过随机阵列对结果进行修正.通过实例验证以及与时间序列方法进行对比,结果证明该方法的预测精度和预测稳定性都有所提高. In order to improve the accuracy of short-term wind speed prediction,this paper combines the wavelet transformation and time series method and takes random components into consideration for short-term wind speed prediction.The wavelet transformation was used to stratify the wind speed time series.After that,the high-frequency-varied wind speed time series were predicted by time series Autoregressive Moving Average Model (ARMA),and the low-frequency-varied wind speed time series were predicted by time series continuous method.The final prediction results were the combination of the above two components,and they were further refined by a method of random array.The example verification and the comparison with the prediction by time series method show that the prediction accuracy and prediction stability of the method are significantly improved.
作者 贾彦 汪尧 王骥飞 赵萌 张驰 李文雄 JIA Yan;WANG Yao;WANG Ji-fei;ZHAO Meng;ZHANG Chi;LI Wen-xiong(College of Energy and Power Engineering,Inner Mongolia University of Technology,Hohhot 010051,China;Ministry of Education Key Laboratory of Wind Energy Utilization Technology,Hohhot 010051,China)
出处 《内蒙古工业大学学报:自然科学版》 2019年第2期115-121,共7页 Journal of Inner Mongolia University of Technology(Natural Science Edition)
基金 内蒙古自治区科技厅科技计划项目(201601064) 风能太阳能利用技术省部共建教育部重点实验室开放基金(201507).
关键词 小波变换 时间序列ARMA 时间序列持续法 随机分量 风速预测 wavelet transform time series ARMA time series continuous method random components wind speed prediction
作者简介 贾彦(1973-),女,副教授,博士,研究方向:新能源开发及利用.
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