[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125002-en":3,"doc-seo-125002-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125002,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning","Weather forecasts from numerical weather prediction models are central inputs to solar energy forecasting, where a physics-based model chain converts solar irradiance forecasts into power production using auxiliary meteorological variables. Ensemble forecasts quantify uncertainty and enable probabilistic solar predictions by propagating uncertainty through the chain, yet ensemble systems often exhibit systematic errors requiring post-processing. This study systematically compares post-processing strategies, including none, step-specific correction, or applying correction in both steps, using the Jacumba solar plant benchmark. Results show substantial forecast improvements, especially when post-processing targets power predictions, with machine learning performing slightly better and a direct neural approach remaining comparable.","arXiv :2406 .04424v1 [ stat .AP] 6 Jun 2024  \nImproving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning  \nNina Horat 1 , Sina Klerings 1 and Sebastian Lerch 1,2  \n1 Karlsruhe Institute of Technology  \n2 Heidelberg Institute for Theoretical Studies  \nJune 10, 2024  \nAbstract  \nWeather 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, using additional weather variables as auxiliary information. Ensemble weather forecasts 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 methods in model chain approaches: 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 post-processing ensemble predictions of global horizontal irradiance 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 postprocessing yield slightly better probabilistic forecasts, and the direct forecasting approach performs comparable to the post-processing strategies.  \n1 Introduction  \nReducing greenhouse gas emissions and mitigating climate change requires a rapid transition towards renewable energy (Van der Meer et al. , 2018) . In addition to wind energy, photovoltaic (PV) solar power plays a pivotal role, with decreasing prices and increasing installed capacity in numerous countries. For example, PV power covered 12 percent of the gross electricity consumption in Germany on average in 2023, and temporarily more than two thirds of the electricity demand on sunny days (Fraunhofer Institute for Solar Energy Systems, 2024) . In light of the volatile nature of renewable energy generation and their increasing importance, accurate and reliable forecasts of power generation from those sources are paramount for managing the electrical grid and to balance demand and supply (Gottwalt et al. , 2016; Appino et al. , 2018) . A  \nkey development in the energy forecasting literature over the past years has been the transition from single-valued deterministic to probabilistic forecasts (Gneiting and Katzfuss, 2014; Hauptet al. , 2019; Yang, 2019; Gneiting et al. , 2023a) which allow for uncertainty quantification and can be issued in the form of probability distributions, quantiles, or prediction intervals (Lauret et al. , 2019; Gneiting et al. , 2023b) .  \nEvidently, weather forecasts from numerical weather prediction (NWP) models are among the most important inputs to models for PV power forecasting. A widely used strategy is the conversion of global horizontal irradiance (GHI) forecasts from an NWP system to PV power forecasts via a model chain, potentially using predictions of other meteorological variables as additional inputs (Roberts et al. , 2017; Mayer and Yang, 2022) . The conversion models typically use several meteorological variables such as GHI, temperature, and wind speed as inputs, and require seve","cbCaioTAQvRE6t96","https://ap.wps.com/l/cbCaioTAQvRE6t96","pdf",8267412,1,22,"English","en",105,"# Introduction\n## Transition to probabilistic energy forecasting\n## Model chains for converting irradiance to PV power\n## Ensemble uncertainty and the need for post-processing","[{\"question\":\"Why are probabilistic solar energy forecasts important for renewable integration?\",\"answer\":\"They quantify uncertainty and help manage grid balancing under volatile renewable generation by providing distributions, quantiles, or prediction intervals.\"},{\"question\":\"What systematic issue in ensemble weather forecasts motivates post-processing?\",\"answer\":\"Ensemble prediction systems are known to exhibit systematic errors, so calibration is required to obtain accurate and reliable probabilistic forecasts.\"},{\"question\":\"Which post-processing strategy yields the strongest improvements in this study?\",\"answer\":\"Applying post-processing to the power predictions produced by the model chain provides the most substantial improvement, while power-focused machine learning methods give slightly better probabilistic forecasts.\"}]","Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning | 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are probabilistic solar energy forecasts important for renewable integration?","Question",{"text":75,"@type":76},"They quantify uncertainty and help manage grid balancing under volatile renewable generation by providing distributions, quantiles, or prediction intervals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What systematic issue in ensemble weather forecasts motivates post-processing?",{"text":80,"@type":76},"Ensemble prediction systems are known to exhibit systematic errors, so calibration is required to obtain accurate and reliable probabilistic forecasts.",{"name":82,"@type":73,"acceptedAnswer":83},"Which post-processing strategy yields the strongest improvements in this study?",{"text":84,"@type":76},"Applying post-processing to the power predictions produced by the model chain provides the most substantial improvement, while power-focused machine learning methods give slightly better probabilistic 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