# Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm

arXiv: https://arxiv.org/abs/2111.15496 · HTML: https://mlsanity.com/paper/2111.15496/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 40 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Neural Network-Based Approach to Phase Space Integration](https://mlsanity.com/paper/1810.11509/) (2018)

## Built on by (most cited first, 6 of 6)

- [Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning](https://mlsanity.com/paper/2204.12404/) (2022)
- [On the hierarchical Bayesian modelling of frequency response functions](https://mlsanity.com/paper/2307.06263/) (2023)
- [Modelling variability in vibration-based PBSHM via a generalised population form](https://mlsanity.com/paper/2203.07115/) (2022)
- [A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes](https://mlsanity.com/paper/2206.11683/) (2022)
- [Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge](https://mlsanity.com/paper/2403.17820/) (2024)
- [Physically-informed change-point kernels for structural dynamics](https://mlsanity.com/paper/2506.11625/) (2025)

## Abstract (arXiv)

Power curves capture the relationship between wind speed and output power for a specific wind turbine. Accurate regression models of this function prove useful in monitoring, maintenance, design, and planning. In practice, however, the measurements do not always correspond to the ideal curve: power curtailments will appear as (additional) functional components. Such multivalued relationships cannot be modelled by conventional regression, and the associated data are usually removed during pre-processing. The current work suggests an alternative method to infer multivalued relationships in curtailed power data. Using a population-based approach, an overlapping mixture of probabilistic regression models is applied to signals recorded from turbines within an operational wind farm. The model is shown to provide an accurate representation of practical power data across the population.