Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting

Abstract

The accurate forecasting 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 forecasting. Many existing studies consider the spatio-temporal correlation of wind speed but ignore the influence of meteorological factors on wind speed with changes in time and space. Therefore, to obtain a reliable and accurate forecasting result, a novel multifactor spatio-temporal correlation model for wind speed forecasting is proposed in this study by combining a convolutional neural network and a long short-term memory neural network. The convolutional neural network is used to extract the spatial feature relationship between the meteorological factors at various sites. The long short-term memory neural network is used to extract the temporal feature relationship between the historical time points. Meanwhile, a new data reconstruction method based on a three-dimensional matrix is developed to represent the proposed multifactor spatio-temporal correlation model. Finally, the datasets collected from the National Wind Institute in Texas, 14 baseline models, 8 evaluation metrics, a performance improvement percentage, and hypothesis testing are used to evaluate the proposed model and provide further discussion comprehensively and scientifically. The experiment results demonstrate that the proposed model outperforms other baseline models in the accuracy of forecasting and the generalization ability.