# Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices

arXiv: https://arxiv.org/abs/2403.05441 · HTML: https://mlsanity.com/paper/2403.05441/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 78 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [False Discoveries Occur Early on the Lasso Path](https://mlsanity.com/paper/1511.01957/) (2015)
- [Distributional neural networks for electricity price forecasting](https://mlsanity.com/paper/2207.02832/) (2022)
- [Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories](https://mlsanity.com/paper/2005.01365/) (2020)
- [Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows](https://mlsanity.com/paper/2205.13826/) (2022)
- [Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices](https://mlsanity.com/paper/2010.01844/) (2020)
- [Probabilistic forecasting of German electricity imbalance prices](https://mlsanity.com/paper/2205.11439/) (2022)
- [Forecasting the Intra-Day Spread Densities of Electricity Prices](https://mlsanity.com/paper/2002.10566/) (2020)
- [A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator](https://mlsanity.com/paper/2111.13609/) (2021)
- [Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution](https://mlsanity.com/paper/2211.13002/) (2022)

## Built on by (most cited first, 4 of 4)

- [OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting](https://mlsanity.com/paper/2502.06830/) (2025)
- [The Living Forecast: Evolving Day-Ahead Predictions into Intraday Reality](https://mlsanity.com/paper/2510.12271/) (2025)
- [The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets](https://mlsanity.com/paper/2511.05523/) (2025)
- [Trend-Aware Multi-Task Learning for Short-Term Energy Forecasting](https://mlsanity.com/paper/2511.09789/) (2025)

## 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.