基于CNN-LSTM的多因素时空风速预测

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...

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

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.