Towards an approach to multivariate outlier detection for District Heating System data
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Computer Science > Machine Learning
Title:Towards an approach to multivariate outlier detection for District Heating System data
Abstract:In this paper, we test different methods for multivariate detection of outliers in the data of transmitted heat energy in the selected substation of local District Heating System, by also considering outside ambient temperature, namely Z-score (univariate, as a benchmark), Mahalanobis distances, Principal Component Analysis (PCA), Isolation Forest and Hotelling's T-squared test. The overall research aims at uncovering irregular plant operation, with a wider objective of identifying the opportunities for reducing the consumption of gas in central heating plants as well as the CO2 emission. The proposed approach considers specific domain circumstances, such as irrelevance of zero transmit-ted energy timepoints as indication of off-grid plant. The outcomes of the different methods are discussed with domain experts. It was concluded that PCA, Isolation Forest and Hotelling method provide relevant results. Finally, we adopt the ensemble method (selection based on the agreement of all three methods on the detected outliers) as the final approach.
| Comments: | 10 pages, 4 figures. This preprint corresponds to the paper published in Lecture Notes in Networks and Systems, vol. 860 (ICIST 2024), Springer |
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2608.11375 [cs.LG] |
| (or arXiv:2608.11375v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11375
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Lecture Notes in Networks and Systems, Vol. 860 (ICIST 2024), Springer, 2024 |
| Related DOI: | https://doi.org/10.1007/978-3-031-71419-1_5
DOI(s) linking to related resources
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From: Milan Zdravković [view email][v1] Tue, 11 Aug 2026 19:29:01 UTC (3,224 KB)
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