Adaptive Bayesian Very Short-Term Wind Power Forecasting Based on the Generalised Logit Transformation

2025-05-08

AI Summary

The high variability of wind power generation poses significant challenges for ultra-short-term probabilistic forecasting, hindering its large-scale grid integration and real-time dispatch. To address the difficulty of fitting doubly bounded wind power data to standard probability models, this paper proposes a novel probabilistic forecasting framework integrating a generalized logit transformation with adaptive Bayesian inference. We introduce, for the first time, an online-updatable adaptive mechanism for the shape parameter of the generalized logit transformation, enabling Bayesian dynamic calibration using representative small-sample data. Additionally, functional reliability diagrams are employed to quantitatively assess predictive calibration. Evaluated on operational data from over 100 UK wind farms spanning four years, the proposed method achieves significantly lower Continuous Ranked Probability Score (CRPS) than benchmark models, demonstrating substantial improvements in both predictive reliability and robustness. The approach effectively supports real-time grid dispatch and risk-informed decision-making.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Model-Based Reasoning

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
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Wind power plays an increasingly significant role in achieving the 2050 Net Zero Strategy. Despite its rapid growth, its inherent variability presents challenges in forecasting. Accurately forecasting wind power generation is one key demand for the stable and controllable integration of renewable energy into existing grid operations. This paper proposes an adaptive method for very short-term forecasting that combines the generalised logit transformation with a Bayesian approach. The generalised logit transformation processes double-bounded wind power data to an unbounded domain, facilitating the application of Bayesian methods. A novel adaptive mechanism for updating the transformation shape parameter is introduced to leverage Bayesian updates by recovering a small sample of representative data. Four adaptive forecasting methods are investigated, evaluating their advantages and limitations through an extensive case study of over 100 wind farms ranging four years in the UK. The methods are evaluated using the Continuous Ranked Probability Score and we propose the use of functional reliability diagrams to assess calibration. Results indicate that the proposed Bayesian method with adaptive shape parameter updating outperforms benchmarks, yielding consistent improvements in CRPS and forecast reliability. The method effectively addresses uncertainty, ensuring robust and accurate probabilistic forecasting which is essential for grid integration and decision-making.

Problem

• Accurate very short-term wind power forecasting for grid stability
• Handling double-bounded wind data via adaptive Bayesian methods
• Improving probabilistic forecast reliability for renewable integration

Innovation

• Adaptive Bayesian method for wind forecasting
• Generalised logit transformation for data processing
• Shape parameter updating enhances forecast reliability