Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929681909940224 |
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| author | Souza, Francisco Badings, Thom Postma, Geert Jansen, Jeroen |
| author_facet | Souza, Francisco Badings, Thom Postma, Geert Jansen, Jeroen |
| contents | New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability and satisfying user requirements. In this paper, we present HELIOS, a dedicated artificial intelligence (AI) model designed specifically for modeling the heat load in DHS. HELIOS leverages a combination of established physical principles and expert knowledge, resulting in superior performance compared to existing state-of-the-art models. HELIOS is explainable, enabling enhanced accountability and traceability in its predictions. We evaluate HELIOS against ten state-of-the-art data-driven models in modeling the heat load in a DHS case study in the Netherlands. HELIOS emerges as the top-performing model while maintaining complete accountability. The applications of HELIOS extend beyond the present case study, potentially supporting the adoption of AI by DHS and contributing to sustainable energy management on a larger scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10827 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems Souza, Francisco Badings, Thom Postma, Geert Jansen, Jeroen Systems and Control New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability and satisfying user requirements. In this paper, we present HELIOS, a dedicated artificial intelligence (AI) model designed specifically for modeling the heat load in DHS. HELIOS leverages a combination of established physical principles and expert knowledge, resulting in superior performance compared to existing state-of-the-art models. HELIOS is explainable, enabling enhanced accountability and traceability in its predictions. We evaluate HELIOS against ten state-of-the-art data-driven models in modeling the heat load in a DHS case study in the Netherlands. HELIOS emerges as the top-performing model while maintaining complete accountability. The applications of HELIOS extend beyond the present case study, potentially supporting the adoption of AI by DHS and contributing to sustainable energy management on a larger scale. |
| title | Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2501.10827 |