Renewable energy-related aspects of statistical post-processing

Abstract: By the end of 2025, the renewable energy share of the global electricity capacity reached 49.4% and the new installations are dominated by solar energy. Due to the high volatility of photovoltaic (PV) energy sources, their successful integration into the electrical grid requires accurate short-term PV power forecasts. PV power is predicted 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, so they require some form of statistical post-processing. First, we introduce a general two-step machine learning-based approach to calibrating ensemble forecasts. 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 parametric approaches. Then, we investigate the problem of direct post-processing of PV power ensemble forecasts. With the help of power output data of seven PV power plants in Hungary and corresponding PV power ensemble forecasts obtained from ensemble weather predictions of the European Centre for Medium-Range Weather Forecasts, we compare the skill of various modern parametric and nonparametric approaches. This is a joint work with Ágnes Baran (University of Debrecen), Nina Horat (Karlsruhe Institute of Technology), Sebastian Lerch (Marburg University, HITS), and Martin János Mayer (Budapest University of Technology and Economics).

probabilitystatistics theorydata analysis, statistics and probability

Audience: researchers in the topic

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Asymptotic Methods in Statistics

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