How Regional Wind Characteristics Affect CNN-based wind predictions: Insights from Spatiotemporal Correlation Analysis

This study investigates the impact of spatiotemporal data dimensions on the precision of a wind forecasting model developed using an artificial neural network. Although previous studies have shown that incorporating spatial data can enhance the accuracy of wind forecasting models, few investigations have explored the extent of the improvement owing to different spatial scales in neural network-based predictive models. Additionally, there are limited studies on the optimal temporal length of the input data for these models. To address this gap, this study employs data with various spatiotemporal dimensions as inputs when forecasting wind using 3D-Convolutional Neural Networks (3D-CNN) and assesses their predictive performance. The results indicate that using spatial data of the surrounding area for 3D-CNN training can achieve better predictive performance than using only single-point information. Additionally, multi-time data had a more positive effect on the predictive performance than single-time data. To determine the reasons for this, correlation analyses were used to determine the impact of the spatial and temporal sizes of the training data on the prediction performance. The study found that as the autocorrelation coefficient (ACC) decreased, meaning that there was less similarity over time, the prediction performance decreased. Furthermore, the spatial standard deviation of the ACC also affects the prediction performance. A Pearson correlation coefficient (PCC) analysis was conducted to examine the effect of space on the prediction performance. Through the PCC analysis, we show that local geometric and seasonal wind conditions can influence the forecast capability of a predictive model.