# Exploring the Interpretability of Forecasting Models for Energy Balancing Market

arXiv: https://arxiv.org/abs/2602.00049 · HTML: https://mlsanity.com/paper/2602.00049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 19 Jan 2026.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Extended vehicle energy dataset (eVED): an enhanced large-scale dataset for deep learning on vehicle trip energy consumption](https://mlsanity.com/paper/2203.08630/) (2022)
- [Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications](https://mlsanity.com/paper/2503.03539/) (2025)
- [Privacy-preserving transactive energy systems: Key topics and open research challenges](https://mlsanity.com/paper/2312.11564/) (2023)
- [An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning](https://mlsanity.com/paper/2111.08472/) (2021)
- [Ultra-short-term solar power forecasting by deep learning and data reconstruction](https://mlsanity.com/paper/2509.17095/) (2025)
- [Cenergy3: An API for City Energy 3D Modeling](https://mlsanity.com/paper/2512.06459/) (2025)

## Built on by (most cited first, 1 of 1)

- [Urban Power Grid Topology and Hierarchy Identification from Open Data](https://mlsanity.com/paper/2606.21352/) (2026)

## Abstract (arXiv)

The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand. Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply. While complex machine learning models can achieve high accuracy, their black-box nature severely limits the model interpretability. In this paper, we explore the trade-off between model accuracy and interpretability for the energy balancing market. Particularly, we take the example of forecasting manual frequency restoration reserve (mFRR) activation price in the balancing market using real market data from different energy price zones. We explore the interpretability of mFRR forecasting using two models: extreme gradient boosting (XGBoost) machine and explainable boosting machine (EBM). We also integrate the two models, and we benchmark all the models against a baseline naive model. Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability. Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price. Importantly, EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics. Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.