Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes

• URL: http://arxiv.org/abs/2505.09026v2
• Date: Sun, 09 Nov 2025 15:04:35 GMT
• Title: Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
• Authors: Domniki Ladopoulou, Dat Minh Hong, Petros Dellaportas,
• Abstract summary: We propose a heteroscedastic non-stationary GP framework based on the generalised spectral mixture kernel.<n>We evaluate the proposed model on 10-minute supervisory control and data acquisition (SCADA) measurements.<n>Results highlight the necessity of modelling both non-stationarity and heteroscedasticity in wind power prediction.
• Score: 3.491999371287298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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