# Electricity Price Forecasting in the Irish Balancing Market

arXiv: https://arxiv.org/abs/2402.06714 · HTML: https://mlsanity.com/paper/2402.06714/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [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)
- [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://mlsanity.com/paper/1905.07886/) (2019)
- [Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories](https://mlsanity.com/paper/2005.01365/) (2020)
- [Probabilistic forecasting of German electricity imbalance prices](https://mlsanity.com/paper/2205.11439/) (2022)
- [Probabilistic Forecasting of Imbalance Prices in the Belgian Context](https://mlsanity.com/paper/2106.07361/) (2021)

## Built on by (most cited first, 6 of 6)

- [Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market](https://mlsanity.com/paper/2502.04935/) (2025)
- [Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression](https://mlsanity.com/paper/2412.00123/) (2024)
- [Optimizing Quantile-based Trading Strategies in Electricity Arbitrage](https://mlsanity.com/paper/2406.13851/) (2024)
- [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)

Short-term electricity markets are becoming more relevant due to less-predictable renewable energy sources, attracting considerable attention from the industry. The balancing market is the closest to real-time and the most volatile among them. Its price forecasting literature is limited, inconsistent and outdated, with few deep learning attempts and no public dataset. This work applies to the Irish balancing market a variety of price prediction techniques proven successful in the widely studied day-ahead market. We compare statistical, machine learning, and deep learning models using a framework that investigates the impact of different training sizes. The framework defines hyperparameters and calibration settings; the dataset and models are made public to ensure reproducibility and to be used as benchmarks for future works. An extensive numerical study shows that well-performing models in the day-ahead market do not perform well in the balancing one, highlighting that these markets are fundamentally different constructs. The best model is LEAR, a statistical approach based on LASSO, which outperforms more complex and computationally demanding approaches.