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

TL;DR

CESAR is a novel neural network model combining convolutional autoencoders and echo state networks for high-resolution wind forecasting, improving accuracy and enabling efficient uncertainty quantification.

Contribution

This work introduces CESAR, a new spatio-temporal neural network architecture that enhances wind speed and power forecasting at high resolution with improved accuracy.

Findings

CESAR outperforms existing methods by up to 17% in wind speed and power prediction.

The model effectively captures complex spatio-temporal dependencies in wind data.

Uncertainty quantification is integrated into the forecasting process.

Abstract

An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo State AutoencodeR (CESAR), a spatio-temporal, neural network-based model which first extracts the spatial features with a deep convolutional autoencoder, and then models their dynamics with an echo state network. We also propose a two-step approach to also allow for computationally affordable inference, while also performing uncertainty quantification. We focus on a high-resolution simulation in Riyadh (Saudi Arabia), an area where wind farm planning is currently ongoing, and show how CESAR is able to provide improved forecasting of wind speed and power for proposed building sites by up to 17% against the best alternative methods.