Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows

Cramer, Eike; Dahmen, Manuel; Witthaut, Dirk; Mitsos, Alexander

doi:10.48550/ARXIV.2205.13826

Preprint

FZJ-2023-00758

Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows

Cramer, E.FZJ*RWTH* ; Witthaut, D.FZJ* ; Mitsos, A.FZJ*RWTH* ; Dahmen, M. (Corresponding author)FZJ*

2022
arXiv

arXiv (2022) [10.48550/ARXIV.2205.13826]

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Please use a persistent id in citations: http://hdl.handle.net/2128/33640 doi:10.48550/ARXIV.2205.13826

Abstract: 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.

Keyword(s): Machine Learning (cs.LG) ; FOS: Computer and information sciences

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