  This paper investigates the influence of incorporating spatiotemporal wind
data on the performance of wind forecasting neural networks. While previous
studies have shown that including spatial data enhances the accuracy of such
models, limited research has explored the impact of different spatial and
temporal scales of input wind data on the learnability of neural network
models. In this study, convolutional neural networks (CNNs) are employed and
trained using various scales of spatiotemporal wind data. The research
demonstrates that using spatiotemporally correlated data from the surrounding
area and past time steps for training a CNN favorably affects the predictive
performance of the model. The study proposes correlation analyses, including
autocorrelation and Pearson correlation analyses, to unveil the influence of
spatiotemporal wind characteristics on the predictive performance of different
CNN models. The spatiotemporal correlations and performances of CNN models are
investigated in three regions: Korea, the USA, and the UK. The findings reveal
that regions with smaller deviations of autocorrelation coefficients (ACC) are
more favorable for CNNs to learn the regional and seasonal wind
characteristics. Specifically, the regions of Korea, the USA, and the UK
exhibit maximum standard deviations of ACCs of 0.100, 0.043, and 0.023,
respectively. The CNNs wind prediction performances follow the reverse order of
the regions: UK, USA, and Korea. This highlights the significant impact of
regional and seasonal wind conditions on the performance of the prediction
models.
