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

2024-10-26

arXiv.org

AI Summary

Ultra-large offshore wind farms require joint probabilistic forecasting of wind speed across multiple heights, spatial locations, and time steps—a challenge inadequately addressed by conventional single-point, single-height forecasting paradigms. Method: We propose the first physics-informed deep learning model based on multi-output integro-difference equations (IDEs), integrating atmospheric dynamics via a learnable, state-dependent, non-stationary kernel that explicitly encodes wind advection vectors. This framework ensures physically consistent spatiotemporal probabilistic prediction. Contribution/Results: Evaluated on high-resolution observational data from the U.S. Northeast, our model achieves statistically significant improvements in both wind speed and power forecasting accuracy over state-of-the-art time-series models, spatiotemporal baselines, and deep learning approaches. It enables high-confidence, multi-height coordinated scheduling decisions for offshore wind farm operations.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

To unlock access to stronger winds, the offshore wind industry is advancing with significantly larger and taller wind turbines. This massive upscaling motivates a departure from univariate 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, and state-dependent 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 future offshore wind energy sites 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.

Problem

• Forecasting offshore wind speeds across multiple heights and locations
• Modeling wind field physics using deep learning and statistical methods
• Improving probabilistic wind energy predictions for ultra-scale turbines

Innovation

• Multi-output integro-difference equation model for forecasting
• Deep learning architecture learns advection vectors
• Probabilistic multi-height space-time wind forecasting