标题： 探索能源平衡市场预测模型的可解释性 显示英文标题

标题： Exploring the Interpretability of Forecasting Models for Energy Balancing Market

摘要： 在能源部门的平衡市场在物理和财务上平衡供需方面起着关键作用。 对平衡市场的动态进行建模可以为电网稳定性和安全能源供应提供有价值的见解和预测。 虽然复杂的机器学习模型可以实现高精度，但其黑箱特性严重限制了模型的可解释性。 在本文中，我们探讨了能源平衡市场中模型精度与可解释性之间的权衡。 特别是，我们以利用不同能源价格区域的真实市场数据来预测平衡市场中手动频率恢复备用（mFRR）激活价格为例。 我们使用两种模型探索mFRR预测的可解释性：极端梯度提升（XGBoost）机器和可解释梯度提升机器（EBM）。 我们还将两种模型结合起来，并将所有模型与一个基线朴素模型进行基准比较。 我们的结果表明，EBM在预测精度方面与XGBoost相当，同时提供了相当高水平的可解释性。 我们的分析还强调了在激活价格与现货价格显著偏离的情况下准确预测mFRR价格的挑战。 重要的是，EBM的可解释性特征揭示了非线性mFRR价格驱动因素和地区市场动态的见解。 我们的研究证明，EBM是平衡市场预测中复杂黑箱AI模型的一个可行且有价值的可解释替代方案。 显示英文摘要

摘要： 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.