Machine learning-based parametric post-processing of solar irradiance ensemble forecasts
- University of Debrecen, Faculty of Informatics, Department of Applied Mathematics and Probability Theory, Debrecen, Hungary (baran.sandor@inf.unideb.hu)
By the end of 2022, the renewable energy share of the global electricity capacity reached 40.3% and the new installations were dominated by solar energy, showing a global increase of 21.7%. Due to the high volatility of photovoltaic energy sources, their successful integration into the electrical grid requires accurate short-term power forecasts. These forecasts are obtained from the predictions of solar irradiance, where the most advanced method is the probabilistic approach based on ensemble forecasts. However, ensemble forecasts are often underdispersive and subject to systematic bias. Hence, they require some form of statistical post-processing, where parametric models provide full predictive distributions of the weather variables at hand.
We propose a general two-step machine learning-based approach to calibrating ensemble weather forecasts, where, in the first step, improved point forecasts are generated, which then together with various ensemble statistics serve as input features of the neural network estimating the parameters of the predictive distribution [1]. In a case study based on global horizontal irradiance forecasts of the operational ensemble prediction system of the Hungarian Meteorological Service, the predictive performance of this novel method is compared with the forecast skill of the raw ensemble and the state-of-the-art ensemble model output statistics approaches [2]. We show that at least up to 48h, statistical post-processing substantially improves the predictive performance of the raw ensemble for all forecast horizons considered; the maximal gain e.g. in terms of the mean continuous ranked probability score is above 20%. Furthermore, the proposed two-step machine learning-based approach outperforms in skill its competitors.
References
1. Baran, Á, Baran, S., A two-step machine-learning approach to statistical post-processing of weather forecasts for power generation. Q. J. R. Meteorol. Soc. (2023), doi:10.1002/qj.4635.
2. Schulz, B., El Ayari, M., Lerch, S., Baran, S., Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting. Sol. Energy 220 (2021), 1016-1031.
*Research is supported by the Hungarian National Research, Development and Innovation Office under grant no. K142849
How to cite: Baran, S. and Baran, Á.: Machine learning-based parametric post-processing of solar irradiance ensemble forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4404, https://doi.org/10.5194/egusphere-egu24-4404, 2024.
