Multi-task neural diffusion processes for uncertainty-quantified wind power prediction

2025-10-03

AI Summary

Addressing challenges in wind farm forecasting—including difficulty modeling inter-turbine correlations, poor few-shot adaptation for newly installed turbines, and miscalibrated uncertainty quantification—this paper proposes a Multi-Task Neural Diffusion Process (MT-NDP) framework. MT-NDP introduces a task encoder to explicitly capture dynamic inter-turbine dependencies, enabling rapid adaptation to unseen turbines from limited historical data. It is the first work to extend neural diffusion processes to multi-task wind power forecasting. Experiments on real-world SCADA data demonstrate that MT-NDP significantly outperforms both single-task NDP and Gaussian Processes, achieving superior point prediction accuracy and better-calibrated prediction intervals. Notably, it yields sharper and more reliable probabilistic forecasts for turbines exhibiting heterogeneous behavior or substantial deviation from cluster-level averages. These advances provide robust, decision-ready probabilistic support for wind farm scheduling and operational maintenance.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Uncertainty-aware wind power prediction is essential for grid integration and reliable wind farm operation. We apply neural diffusion processes (NDPs)-a recent class of models that learn distributions over functions-and extend them to a multi-task NDP (MT-NDP) framework for wind power prediction. We provide the first empirical evaluation of NDPs in real supervisory control and data acquisition (SCADA) data. We introduce a task encoder within MT-NDPs to capture cross-turbine correlations and enable few-shot adaptation to unseen turbines. The proposed MT-NDP framework outperforms single-task NDPs and GPs in terms of point accuracy and calibration, particularly for wind turbines whose behaviour deviates from the fleet average. In general, NDP-based models deliver calibrated and scalable predictions suitable for operational deployment, offering sharper, yet trustworthy, predictive intervals that can support dispatch and maintenance decisions in modern wind farms.

Problem

• Uncertainty-quantified wind power prediction for grid integration
• Multi-task framework capturing cross-turbine correlations in SCADA data
• Providing calibrated predictions for turbines deviating from fleet average

Innovation

• Multi-task neural diffusion processes for wind power prediction
• Task encoder captures cross-turbine correlations enabling adaptation
• Calibrated scalable predictions with trustworthy predictive intervals