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

arXiv: https://arxiv.org/abs/2410.20166 · HTML: https://mlsanity.com/paper/2410.20166/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 76 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Deep Kalman Filters](https://mlsanity.com/paper/1511.05121/) (2015)
- [Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge](https://mlsanity.com/paper/1711.07970/) (2017)
- [Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting](https://mlsanity.com/paper/1910.13524/) (2019)
- [Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting](https://mlsanity.com/paper/1707.08110/) (2017)
- [Wind Energy: Forecasting Challenges for Its Operational Management](https://mlsanity.com/paper/1312.6471/) (2013)
- [Risk-Aware Control and Optimization for High-Renewable Power Grids](https://mlsanity.com/paper/2204.00950/) (2022)
- [Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures](https://mlsanity.com/paper/2208.13585/) (2022)
- [Seizing Opportunity: Maintenance Optimization in Offshore Wind Farms Considering Accessibility, Production, and Crew Dispatch](https://mlsanity.com/paper/2012.00213/) (2020)
- [Multivariate spatio-temporal models for high-dimensional areal data with application to Longitudinal Employer-Household Dynamics](https://mlsanity.com/paper/1503.00982/) (2015)
- [Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections](https://mlsanity.com/paper/2205.10943/) (2022)
- [STOCHOS: Stochastic Opportunistic Maintenance Scheduling For Offshore Wind Farms](https://mlsanity.com/paper/2207.02274/) (2022)
- [AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. Mid Atlantic Offshore Wind Energy Areas](https://mlsanity.com/paper/2303.02246/) (2023)
- [Joint Optimization of Production and Maintenance in Offshore Wind Farms: Balancing the Short- and Long-Term Needs of Wind Energy Operation](https://mlsanity.com/paper/2303.06174/) (2023)

## Built on by (most cited first, 2 of 2)

- [Observation-driven correction of numerical weather prediction for marine winds](https://mlsanity.com/paper/2512.03606/) (2025)
- [A Deep Zero-Inflated Model of North Atlantic Right Whale Presence To Support Blue Economy Management in the U.S. East Coast](https://mlsanity.com/paper/2606.14403/) (2026)

## Abstract (arXiv)

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.