What question did this study set out to answer?

To develop a Bayesian framework for projecting future wind turbine characteristics while formally quantifying uncertainty.

June 27, 2026Open Access

A Bayesian Framework for Probabilistic Wind Turbine Technology Projections: Multi-Region Validation and Application to Climate-Aware Energy Yield Estimation

Key Points

To develop a Bayesian framework for projecting future wind turbine characteristics while formally quantifying uncertainty.
Applied a Bayesian logistic model to project turbine characteristics using data from multiple regions.
Validated across Austria, Germany, and the United States with a total of over 100,000 turbines.
Implemented Markov Chain Monte Carlo sampling for full posterior predictive distributions.
Demonstrated superior hindcast performance compared to linear and polynomial models.
Identified that technology uncertainty is greater than climate uncertainty in energy yield estimates until full inter-model climate spread is considered.

Abstract

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows.

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