Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes

2025/05/13 by Domniki Ladopoulou, Ladopoulou, Domniki, Hong, Dat Minh +2 · 1 citation
Computer Science · Engineering · #Applications (stat.AP) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2505.09026

openalex publication_date 2025/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring
• Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case
• Weight Uncertainty in Neural Networks
• Strictly Proper Scoring Rules, Prediction, and Estimation

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