Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction

2025-02-11

AI Summary

To address low accuracy, poor interpretability, and difficulty in modeling nonlinear relationships in wind power forecasting, this paper proposes a physics-guided and data-driven hybrid semi-parametric model. A parametric base model is constructed based on the physical wind speed–power relationship, while a nonparametric residual module—implemented via XGBoost or LightGBM—captures unmodeled influences. We introduce, for the first time, a synergistic architecture integrating a physics-based submodel with a SHAP-explained residual module, further enhanced by conformal quantile regression (CQR) for rigorous uncertainty quantification. Evaluated on real-world measurements from a four-turbine wind farm, the model reduces average prediction error by 37% compared to a pure physics-based baseline. It thus significantly improves support for fault early warning and operational optimization, achieving high accuracy, strong interpretability, and reliable uncertainty estimation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Low Level & Physics-based Vision

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed, they often fail to capture the complex, non-linear relationships between other input variables and the resulting power output. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling by leveraging large datasets, enhancing prediction accuracy but often at the cost of interpretability. In this study, we propose a hybrid semi-parametric model that combines the strengths of both approaches, applied to a dataset from a wind farm with four turbines. The model integrates a physics-inspired submodel, providing a reasonable approximation of power generation, with a non-parametric submodel that predicts the residuals. This non-parametric submodel is trained on a broader range of variables to account for phenomena not captured by the physics-based component. The hybrid model achieves a 37% improvement in prediction accuracy over the physics-based model. To enhance interpretability, SHAP values are used to analyze the influence of input features on the residual submodel's output. Additionally, prediction uncertainties are quantified using a conformalized quantile regression method. The combination of these techniques, alongside the physics grounding of the parametric submodel, provides a flexible, accurate, and reliable framework. Ultimately, this study opens the door for evaluating the impact of unmodeled variables on wind turbine power generation, offering a basis for potential optimization.

Problem

• Optimize wind turbine operations and maintenance.
• Improve power prediction accuracy using hybrid models.
• Enhance interpretability and quantify prediction uncertainties.

Innovation

• Hybrid semi-parametric model
• SHAP values for interpretability
• Conformalized quantile regression