用可解释AI分析供暖需求模型的全局特征重要性，提升系统可信度。
On the global feature importance for interpretable and trustworthy heat demand forecasting
- 采用梯度提升等四种无扰动的可解释方法评估特征重要性
- 结果揭示关键影响因素，如气温与历史负荷对预测的主导作用
- 适合高风险场景下需透明决策的智能供热系统应用
本文提出一种事前可解释AI方法，用于评估用于区域供热系统智能控制的机器学习模型在供暖需求预测中的全局特征重要性，旨在提升模型的可解释性与可信度，缓解遵守社区标准、客户满意度及责任风险等问题。方法包括梯度提升的内在可解释性以及三种事后可解释方法：部分依赖图、累积局部效应和SHAP，均不依赖特征置换或扰动，避免引入随机且不现实的数据值所导致的偏差。结果讨论强调了各方法间的互补性，并结合区域供热过程给出具体解读。
原文摘要 · Abstract (English)
The paper introduces the ante-hoc Explainable AI methodology to assess the global feature importance of the Machine Learning models used for heat demand forecasting in intelligent control of District Heating Systems, with motivation to facilitate their interpretability and trustworthiness, hence addressing the challenges related to adherence to communal standards, customer satisfaction and liability risks. Methodology includes use of four different approaches, namely intrinsic interpretability of Gradient Boosting method and selected post-hoc methods, namely Partial Dependence, Accumulated Local Effects and SHAP. None of the selected methods assume feature permutation or perturbations which can introduce bias due to introduction of random unrealistic values of data instances. Discussion of results is provided, including the assessment of complementarities where applicable, with specific interpretations in context of the district heating processes.
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