Effects of spatiotemporal correlations in wind data on neural network-based wind predictions

TL;DR

This study examines how incorporating spatiotemporal wind data at different scales affects neural network wind prediction accuracy across regions, highlighting the importance of regional wind characteristics for model performance.

Contribution

It introduces correlation analysis methods to evaluate the impact of spatiotemporal wind data scales on CNN-based wind prediction across multiple regions.

Findings

Regions with smaller autocorrelation deviations improve CNN learning.

UK region shows best prediction performance due to wind characteristics.

Spatiotemporal correlations significantly influence wind prediction accuracy.

Abstract

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.