AI-based approach for short-term forecasting of wind speed from a weather station network: A Case study in Valencia

The generation of accurate and reliable forecasts of near-surface (~10 m above ground level) gridded wind speed data, hereinafter called NSWS, is crucial since it influences numerous socioeconomic and environmental fields. For instance, in the face of climate change, wind energy can contribute to the decarbonization of the electricity grid. NSWS, however, is a complex meteorological variable due to its inherent space-time variability, particularly in regions with complex topography like Valencia (Spain).The traditional approach to forecasting NSWS relies on Numerical Weather Prediction (NWP) models, which demand substantial computational resources, specially when high spatial and temporal resolution are required, often necessitating hundred to thousands of CPU hours. As an innovative solution to this pressing issue, the ThinkInAzul project, under Climatoc-Lab, is exploring the use of deep learning for accurate NSWS predictions. We propose an architecturebased on encoder-decoder neural networks composing mixed convolutional and recurrent (ConvLSTM) layers. This AI-based product, designed as an early warning system, generate high-resolution (3- or 9-km) short-term (i.e.,