Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning  

作　　者：Nina HORAT Sina KLERINGS Sebastian LERCH 

机构地区：[1]Institute of Statistics,Karlsruhe Institute of Technology,Karlsruhe 76185,Germany [2]Heidelberg Institute for Theoretical Studies,Heidelberg 69118,Germany

出　　处：《Advances in Atmospheric Sciences》2025年第2期297-312,共16页大气科学进展(英文版)

基　　金：the Young Investigator Group“Artificial Intelligence for Probabilistic Weather Forecasting”funded by the Vector Stiftung;funding from the Federal Ministry of Education and Research(BMBF)and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments。

摘　　要：Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.

关 键 词：solar forecasting POST-PROCESSING probabilistic forecasting machine learning model chain 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TK511[自动化与计算机技术—控制科学与工程]