Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
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| Format: | Preprint |
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2025
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| _version_ | 1866914143102042112 |
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| author | Ladopoulou, Domniki Hong, Dat Minh Dellaportas, Petros |
| author_facet | Ladopoulou, Domniki Hong, Dat Minh Dellaportas, Petros |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_09026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes Ladopoulou, Domniki Hong, Dat Minh Dellaportas, Petros Applications Machine Learning 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. |
| title | Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2505.09026 |