Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes

TL;DR

This paper introduces a heteroscedastic non-stationary Gaussian process model with a spectral mixture kernel for improved probabilistic wind power prediction, capturing input-dependent correlations and variability.

Contribution

The paper presents a novel GP framework that models non-stationarity and heteroscedasticity in wind power data using a generalized spectral mixture kernel.

Findings

The proposed model outperforms stationary GP variants in wind power prediction.

Model captures input-dependent correlations and variability effectively.

Demonstrates practical utility in operational SCADA data analysis.

Abstract

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.