# Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes

arXiv: https://arxiv.org/abs/2505.09026 · HTML: https://mlsanity.com/paper/2505.09026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 5 of 5)

- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [Gaussian Process Kernels for Pattern Discovery and Extrapolation](https://mlsanity.com/paper/1302.4245/) (2013)
- [Non-Stationary Spectral Kernels](https://mlsanity.com/paper/1705.08736/) (2017)
- [Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case](https://mlsanity.com/paper/2203.07080/) (2022)
- [Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring](https://mlsanity.com/paper/2404.16496/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-task Neural Diffusion Processes](https://mlsanity.com/paper/2510.03419/) (2025)

## Abstract (arXiv)

Accurate probabilistic prediction of wind power is crucial for maintaining grid stability and facilitating the efficient integration of renewable energy sources. Gaussian process (GP) models offer a principled framework for quantifying uncertainty; however, conventional approaches typically rely on stationary kernels and homoscedastic noise assumptions, which are inadequate for modelling the inherently non-stationary and heteroscedastic nature of wind speed and power output. We propose a heteroscedastic non-stationary GP framework based on the generalised spectral mixture kernel, enabling the model to capture input-dependent correlations as well as input-dependent variability in wind speed-power data. We evaluate the proposed model on 10-minute supervisory control and data acquisition (SCADA) measurements and compare it against GP variants with stationary and non-stationary kernels, as well as commonly used non-GP probabilistic baselines. The results highlight the necessity of modelling both non-stationarity and heteroscedasticity in wind power prediction and demonstrate the practical value of flexible non-stationary GP models in operational SCADA settings.