On the global feature importance for interpretable and trustworthy heat demand forecasting
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Computer Science > Machine Learning
Title:On the global feature importance for interpretable and trustworthy heat demand forecasting
Abstract: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.
| Comments: | 9 pages, 5 figures. This preprint corresponds to the paper published in Thermal Science 2025 Volume 29, Issue 5 Part A, Pages: 3355-3365 |
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2608.13039 [cs.LG] |
| (or arXiv:2608.13039v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.13039
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Thermal Science 2025 Volume 29, Issue 5 Part A, Pages: 3355-3365 |
| Related DOI: | https://doi.org/10.2298/TSCI241223048Z
DOI(s) linking to related resources
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From: Milan Zdravković [view email][v1] Thu, 13 Aug 2026 10:05:25 UTC (1,160 KB)
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