Opportunities for Machine Learning in District Heating
The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the las...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/13/6112 |
| _version_ | 1797528132635328512 |
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| author | Gideon Mbiydzenyuy Sławomir Nowaczyk Håkan Knutsson Dirk Vanhoudt Jens Brage Ece Calikus |
| author_facet | Gideon Mbiydzenyuy Sławomir Nowaczyk Håkan Knutsson Dirk Vanhoudt Jens Brage Ece Calikus |
| author_sort | Gideon Mbiydzenyuy |
| collection | DOAJ |
| description | The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry. |
| first_indexed | 2024-03-10T09:53:47Z |
| format | Article |
| id | doaj.art-6de0f0420d4b49669afed280c1b4c7bf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:53:47Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6de0f0420d4b49669afed280c1b4c7bf2023-11-22T02:30:26ZengMDPI AGApplied Sciences2076-34172021-06-011113611210.3390/app11136112Opportunities for Machine Learning in District HeatingGideon Mbiydzenyuy0Sławomir Nowaczyk1Håkan Knutsson2Dirk Vanhoudt3Jens Brage4Ece Calikus5Department of Information Technology, University of Borås, SE-501 90 Boras, SwedenCAISR, University of Halmstad, SE-301 18 Halmstad, SwedenThe School of Business, Engineering and Science, University of Halmstad, SE-301 18 Halmstad, SwedenVITO, Boeretang 200, 2400 Mol, BelgiumNODA Intelligent Systems, SE-374 35 Karlshamn, SwedenCAISR, University of Halmstad, SE-301 18 Halmstad, SwedenThe district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry.https://www.mdpi.com/2076-3417/11/13/6112Machine Learningdistrict heatingreviewroad-mapresearch opportunities |
| spellingShingle | Gideon Mbiydzenyuy Sławomir Nowaczyk Håkan Knutsson Dirk Vanhoudt Jens Brage Ece Calikus Opportunities for Machine Learning in District Heating Applied Sciences Machine Learning district heating review road-map research opportunities |
| title | Opportunities for Machine Learning in District Heating |
| title_full | Opportunities for Machine Learning in District Heating |
| title_fullStr | Opportunities for Machine Learning in District Heating |
| title_full_unstemmed | Opportunities for Machine Learning in District Heating |
| title_short | Opportunities for Machine Learning in District Heating |
| title_sort | opportunities for machine learning in district heating |
| topic | Machine Learning district heating review road-map research opportunities |
| url | https://www.mdpi.com/2076-3417/11/13/6112 |
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