DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting

TL;DR

DeepMIDE is a novel multi-output deep learning approach that models offshore wind speeds across space, time, and height, improving forecasting accuracy for larger turbines in offshore wind energy.

Contribution

It introduces a multi-output integro-difference equation model with a deep learning component to jointly forecast multi-height offshore wind speeds, capturing physics-based wind propagation.

Findings

Outperforms existing time series, spatio-temporal, and deep learning methods.

Provides probabilistic multi-height wind forecasts.

Demonstrates effectiveness on real-world offshore wind data.

Abstract

To unlock access to stronger winds, the offshore wind industry is advancing towards significantly larger and taller wind turbines. This massive upscaling motivates a departure from wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE--a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate nonstationary kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high-dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from offshore wind energy areas in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.