Probabilistic Wind Power Forecasting via Non-Stationary Gaussian Processes

• URL: http://arxiv.org/abs/2505.09026v1
• Date: Tue, 13 May 2025 23:46:33 GMT
• Title: Probabilistic Wind Power Forecasting via Non-Stationary Gaussian Processes
• Authors: Domniki Ladopoulou, Dat Minh Hong, Petros Dellaportas,
• Abstract summary: We propose a non-stationary GP framework that incorporates the generalized spectral mixture kernel.<n>We evaluate the performance of the proposed model on real-world SCADA data.<n>Results highlight the necessity of modeling non-stationarity in wind power forecasting.
• Score: 4.956709222278242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate probabilistic forecasting of wind power is essential for maintaining grid stability and enabling efficient integration of renewable energy sources. Gaussian Process (GP) models offer a principled framework for quantifying uncertainty; however, conventional approaches rely on stationary kernels, which are inadequate for modeling the inherently non-stationary nature of wind speed and power output. We propose a non-stationary GP framework that incorporates the generalized spectral mixture (GSM) kernel, enabling the model to capture time-varying patterns and heteroscedastic behaviors in wind speed and wind power data. We evaluate the performance of the proposed model on real-world SCADA data across short\mbox{-,} medium-, and long-term forecasting horizons. Compared to standard radial basis function and spectral mixture kernels, the GSM-based model outperforms, particularly in short-term forecasts. These results highlight the necessity of modeling non-stationarity in wind power forecasting and demonstrate the practical value of non-stationary GP models in operational settings.

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