XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems
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Computer Science > Machine Learning
Title:XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems
Abstract:This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (DHS). The objective is to explore scenarios in which conventional ML algorithms demonstrate better performance over deep learning networks in time series forecasting and the associated benefits in terms of computational cost and environmental impact. The study focuses on a real-world DHS dataset. Through experimentation and analysis, it is demonstrated that XGBoost consistently outperforms LSTM in this specific forecasting task. The difference is explained by the error distribution illustrating that LSTM makes more significant errors in the intervals of less data availability. The reduced computational demands of conventional ML approaches not only result in cost savings but also minimize the carbon footprint associated with data analysis tasks in energy systems.
| Comments: | 9 pages, 7 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:2608.11446 [cs.LG] |
| (or arXiv:2608.11446v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11446
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_2
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From: Milan Zdravković [view email][v1] Tue, 11 Aug 2026 21:24:22 UTC (1,244 KB)
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