Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations

TL;DR

This paper introduces a novel spatio-temporal wind modeling approach combining deep echo state networks and stochastic PDEs, improving forecast accuracy for renewable energy planning in Saudi Arabia.

Contribution

It develops a new hybrid model that reduces spatial complexity, captures non-linear dynamics, and reconstructs high-resolution wind data for better energy output predictions.

Findings

Achieves more accurate wind speed and energy forecasts.

Saves up to one million dollars annually compared to existing models.

Effectively models fine-scale wind structures in complex terrains.

Abstract

In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of oil to a reliance on renewable energy, and, in particular, wind. Modeling wind for assessing potential energy output in a country as large, geographically diverse and understudied as Saudi Arabia is a challenge which implies highly non-linear dynamic structures in both space and time. To address this, we propose a spatio-temporal model whose spatial information is first reduced via an energy distance-based approach and then its dynamical behavior is informed by a sparse and stochastic recurrent neural network (Echo State Network). Finally, the full spatial data is reconstructed by means of a non-stationary stochastic partial differential equation-based approach. Our model can capture the fine scale wind structure and produce more accurate forecasts of both wind speed and energy in lead times of interest for energy grid management and save annually as much as one million dollar against the closest competitive model.