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

TL;DR

This paper introduces an adaptive Bayesian wind power forecasting method that uses a generalized logit transformation to improve short-term prediction accuracy and reliability for grid integration.

Contribution

It presents a novel adaptive approach combining the generalized logit transformation with Bayesian updating, enhancing wind power forecast accuracy and reliability.

Findings

Outperforms benchmark methods in CRPS and reliability.

Effectively captures uncertainty in wind power forecasts.

Demonstrates robustness across multiple wind farms over four years.

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.