Exploring the Interpretability of Forecasting Models for Energy Balancing Market

TL;DR

This paper compares the accuracy and interpretability of machine learning models for forecasting energy balancing market prices, highlighting the effectiveness of explainable boosting machines as an interpretable alternative.

Contribution

It demonstrates that explainable boosting machines can achieve comparable accuracy to complex models while providing valuable interpretability insights into energy market dynamics.

Findings

EBM offers similar accuracy to XGBoost with better interpretability

Interpretability features reveal non-linear price drivers and regional market influences

Predicting extreme price deviations remains challenging

Abstract

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.