CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting

Summary

Researchers introduce CESAR, a convolutional echo state autoencoder for high-resolution wind forecasting. The model extracts spatial features using a deep convolutional autoencoder and models their dynamics with an echo state network. Tested on high-resolution simulations in Riyadh, Saudi Arabia, CESAR improved wind speed and power forecasting by up to 17% compared to other methods. Why it matters: Accurate wind forecasting is critical for efficient wind farm planning and management in Saudi Arabia and the broader region.