Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
This work addresses grid stability and renewable energy integration by improving wind power forecasting, though it appears incremental as it extends existing GP methods with specific kernel modifications.
The paper tackled the problem of inaccurate probabilistic wind power prediction by proposing a heteroscedastic non-stationary Gaussian process framework, which outperformed conventional GP variants and non-GP baselines on 10-minute SCADA data, demonstrating the necessity of modeling both non-stationarity and heteroscedasticity.
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.