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| 001 | 917556 | ||
| 005 | 20240712112906.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2205.13826 |2 doi |
| 024 | 7 | _ | |a 2128/33640 |2 Handle |
| 037 | _ | _ | |a FZJ-2023-00758 |
| 100 | 1 | _ | |a Cramer, Eike |0 P:(DE-Juel1)179591 |b 0 |u fzj |
| 245 | _ | _ | |a Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows |
| 260 | _ | _ | |c 2022 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1673945487_26809 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts. The model captures the emerging hourly pattern by considering the four 15min intervals in each day-ahead price interval as a four-dimensional joint probability distribution. The resulting nontrivial, multivariate price difference distribution is learned using a normalizing flow, i.e., a deep generative model that combines conditional multivariate density estimation and probabilistic regression. Furthermore, this work discusses the influence of different external impact factors based on literature insights and impact analysis using explainable artificial intelligence (XAI). The normalizing flow is compared to an informed selection of historical data and probabilistic forecasts using a Gaussian copula and a Gaussian regression model. Among the different models, the normalizing flow identifies the trends with the highest accuracy and has the narrowest prediction intervals. Both the XAI analysis and the empirical experiments highlight that the immediate history of the price difference realization and the increments of the day-ahead price have the most substantial impact on the price difference. |
| 536 | _ | _ | |a 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112) |0 G:(DE-HGF)POF4-1121 |c POF4-112 |f POF IV |x 0 |
| 536 | _ | _ | |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) |0 G:(DE-Juel1)HDS-LEE-20190612 |c HDS-LEE-20190612 |x 1 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Witthaut, Dirk |0 P:(DE-Juel1)162277 |b 1 |u fzj |
| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 2 |u fzj |
| 700 | 1 | _ | |a Dahmen, Manuel |0 P:(DE-Juel1)172097 |b 3 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2205.13826 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/917556/files/2205.13826.pdf |y OpenAccess |
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| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Energie |l Energiesystemdesign (ESD) |1 G:(DE-HGF)POF4-110 |0 G:(DE-HGF)POF4-112 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-100 |4 G:(DE-HGF)POF |v Digitalisierung und Systemtechnik |9 G:(DE-HGF)POF4-1121 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
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| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a preprint |
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| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IEK-10-20170217 |
| 981 | _ | _ | |a I:(DE-Juel1)ICE-1-20170217 |
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