A Methodology for Turbine-Level Possible Power Prediction and Uncertainty Estimations Using Farm-Wide Autoregressive Information on High-Frequency Data

Francisco Javier Jara Ávila (), Timothy Verstraeten, Pieter Jan Daems, Ann Nowé and Jan Helsen
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Francisco Javier Jara Ávila: Acoustics and Vibrations Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Timothy Verstraeten: Acoustics and Vibrations Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Pieter Jan Daems: Acoustics and Vibrations Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Ann Nowé: Artificial Intelligence Lab, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Jan Helsen: Acoustics and Vibrations Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium

Energies, 2025, vol. 18, issue 14, 1-23

Abstract: Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection.

Keywords: wind farm; SCADA; possible power; kernels; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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