mlsanityData to 7 Oct 2026

Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices

arXiv 2403.05441 ↗First posted 8 Mar 2024stat.AP cs.LGcited by 4

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Abstract (arXiv)

We address the need for forecasting methodologies that handle large uncertainties in electricity prices for continuous intraday markets by incorporating parameter uncertainty and using a broad set of covariables. This study presents the first Bayesian forecasting of electricity prices traded on the German intraday market. Endogenous and exogenous covariables are handled via Orthogonal Matching Pursuit (OMP) and regularising priors. The target variable is the IDFull price index, with forecasts given as posterior predictive distributions. Validation uses the highly volatile 2022 electricity prices, which have seldom been studied. As a benchmark, we use all intraday transactions at the time of forecast to compute a live IDFull value. According to market efficiency, it should not be possible to improve on this last-price benchmark. However, we observe significant improvements in point measures and probability scores, including an average reduction of 5.9,% in absolute errors and an average increase of 1.7,% in accuracy when forecasting whether the IDFull exceeds the day-ahead price. Finally, we challenge the use of LASSO in electricity price forecasting, showing that OMP results in superior performance, specifically an average reduction of 22.7,% in absolute error and 20.2,% in the continuous ranked probability score.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 78 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

9 of 78 references.

References · 9

  1. 2015False Discoveries Occur Early on the Lasso Pathcited by 31
  2. 2022Distributional neural networks for electricity price forecastingcited by 24
  3. 2020Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectoriescited by 15
  4. 2022Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flowscited by 14
  5. 2020Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Pricescited by 11
  6. 2022Probabilistic forecasting of German electricity imbalance pricescited by 10
  7. 2020Forecasting the Intra-Day Spread Densities of Electricity Pricescited by 4
  8. 2021A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operatorcited by 3
  9. 2022Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distributioncited by 3

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecastingcited by 2
  2. 2025The Living Forecast: Evolving Day-Ahead Predictions into Intraday Realitycited by 0
  3. 2025The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Marketscited by 0
  4. 2025Trend-Aware Multi-Task Learning for Short-Term Energy Forecastingcited by 0