000917556 001__ 917556
000917556 005__ 20240712112906.0
000917556 0247_ $$2doi$$a10.48550/ARXIV.2205.13826
000917556 0247_ $$2Handle$$a2128/33640
000917556 037__ $$aFZJ-2023-00758
000917556 1001_ $$0P:(DE-Juel1)179591$$aCramer, Eike$$b0$$ufzj
000917556 245__ $$aMultivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows
000917556 260__ $$barXiv$$c2022
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000917556 520__ $$aElectricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts. The model captures the emerging hourly pattern by considering the four 15min intervals in each day-ahead price interval as a four-dimensional joint probability distribution. The resulting nontrivial, multivariate price difference distribution is learned using a normalizing flow, i.e., a deep generative model that combines conditional multivariate density estimation and probabilistic regression. Furthermore, this work discusses the influence of different external impact factors based on literature insights and impact analysis using explainable artificial intelligence (XAI). The normalizing flow is compared to an informed selection of historical data and probabilistic forecasts using a Gaussian copula and a Gaussian regression model. Among the different models, the normalizing flow identifies the trends with the highest accuracy and has the narrowest prediction intervals. Both the XAI analysis and the empirical experiments highlight that the immediate history of the price difference realization and the increments of the day-ahead price have the most substantial impact on the price difference.
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000917556 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x1
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000917556 650_7 $$2Other$$aMachine Learning (cs.LG)
000917556 650_7 $$2Other$$aFOS: Computer and information sciences
000917556 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b1$$ufzj
000917556 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b2$$ufzj
000917556 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b3$$eCorresponding author$$ufzj
000917556 773__ $$a10.48550/ARXIV.2205.13826
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000917556 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1121$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x0
000917556 9141_ $$y2022
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000917556 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
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