# Probabilistic forecasting of German electricity imbalance prices

arXiv: https://arxiv.org/abs/2205.11439 · HTML: https://mlsanity.com/paper/2205.11439/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 66 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 10 of 10)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks](https://mlsanity.com/paper/1805.06649/) (2018)
- [CRPS Learning](https://mlsanity.com/paper/2102.00968/) (2021)
- [Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories](https://mlsanity.com/paper/2005.01365/) (2020)
- [M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond](https://mlsanity.com/paper/2107.06675/) (2021)
- [Probabilistic Forecasting of Imbalance Prices in the Belgian Context](https://mlsanity.com/paper/2106.07361/) (2021)
- [Forecasting day-ahead electricity prices in Europe: the importance of considering market integration](https://mlsanity.com/paper/1708.07061/) (2017)
- [Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity](https://mlsanity.com/paper/1606.00546/) (2016)
- [tsrobprep - an R package for robust preprocessing of time series data](https://mlsanity.com/paper/2104.12657/) (2021)

## Built on by (most cited first, 10 of 10)

- [Electricity Price Forecasting in the Irish Balancing Market](https://mlsanity.com/paper/2402.06714/) (2024)
- [Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices](https://mlsanity.com/paper/2403.05441/) (2024)
- [Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search](https://mlsanity.com/paper/2411.04011/) (2024)
- [Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market](https://mlsanity.com/paper/2502.04935/) (2025)
- [Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks](https://mlsanity.com/paper/2512.14779/) (2025)
- [Optimizing Quantile-based Trading Strategies in Electricity Arbitrage](https://mlsanity.com/paper/2406.13851/) (2024)
- [Learn to Bid as a Price-Maker Wind Power Producer](https://mlsanity.com/paper/2503.16107/) (2025)
- [ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market](https://mlsanity.com/paper/2503.17214/) (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)
- [A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting](https://mlsanity.com/paper/2605.09061/) (2026)

## Abstract (arXiv)

The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. Therefore, in this manuscript we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. The analysis was conducted on the German market, however it could be easily applied to any other market of similar structure.