基于CNN-LSTM的多因素时空风速预测

Multifactor Spatio-Temporal Wind Speed Prediction Based on CNN-LSTM

准确的风速预测在风能转换和电力分配中起着至关重要的作用.但是,风的固有间歇性使其难以实现高精度的预测.现有研究方法大都考虑了风速的时间相关性,但忽略了气象因素随空间变化对风速的影响.为获得准确可靠的预测结果,结合卷积神经网络和长短期记忆网络,提出了一种多因素时空风速预测相关(MFSTC)模型.同时,还构建了一种基于三维矩阵的数据表示方法.针对多个站点,利用改进的PCA-LASSO算法提取特征气象要素,然后,采用卷积神经网络建立各个站点之间的空间特征关系,采用长短期记忆网络建立历史时间点之间的时间特征关系,在时空相关性分析的基础上得到最终风速预测结果.在东营气象中心提供的2009–2018共10年的实测风速数据集上进行了实验验证.结果表明,相比于一般预测方法,由MFSTC模型获得的实验结果更加准确,证明了提出方法的有效性.

The accurate prediction of wind speed plays a vital role in the transformation of wind energy and the dispatching of electricity.However,the inherent intermittence of wind makes it a challenge to achieve high-precision wind speed prediction.Most studies consider the temporal correlation of wind speed but ignore the influence of meteorological factors with changes in space on wind speed.To obtain accurate and reliable forecasting results,this study proposes a MultiFactor Spatio-Temporal Correlation(MFSTC)model for wind speed prediction by combining a Convolutional Neural Network(CNN)and a Long Short-Term Memory(LSTM)network.This paper also constructs a data representation method based on a three-dimensional matrix.For multiple sites,this model employs the improved PCA-LASSO algorithm to extract the characteristic meteorological factors.Then,it uses CNN to establish the spatial feature relationship among the sites and the LSTM network to establish the temporal feature relationship among historical time points.The final wind speed prediction results are obtained based on spatio-temporal correlation analysis.Furthermore,experimental verification is carried out on the 10 years of actual wind speed datasets from 2009 to 2018 provided by Dongying Meteorological Center.The results show that the MFSTC model is more accurate than common prediction methods,which proves the effectiveness of the proposed method.