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Opportunities for Machine Learning in District Heating
Department of Information Technology, University of Borås, Borås, Sweden.ORCID iD: 0000-0002-9685-7775
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Business, Innovation and Sustainability.
VITO, Mol, Belgium | EnergyVille, Genk, Belgium.
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2021 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 11, no 13, article id 6112Article in journal (Refereed) Published
Abstract [en]

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
Basel: MDPI, 2021. Vol. 11, no 13, article id 6112
Keywords [en]
Machine Learning, district heating, review, road-map, research opportunities
National Category
Energy Systems Energy Engineering
Identifiers
URN: urn:nbn:se:hh:diva-45190DOI: 10.3390/app11136112ISI: 000672314400001Scopus ID: 2-s2.0-85110014396OAI: oai:DiVA.org:hh-45190DiVA, id: diva2:1576333
Projects
Data Analytics for Fault Detection in District Heating (DAD)Self-Monitoring for Innovation (SeMI)SAM
Funder
Knowledge Foundation, 20170182Vinnova, 2018-03349
Note

Funding: The work presented in this article was financed by the Swedish Knowledge Foundation (KK-stiftelsen http://www.kks.se/om-oss/in-english/ accessed on 15 May 2021) under grant Dnr.20170182 within the project Data Analytics for Fault Detection in District Heating (DAD) and grant Dnr.20160301 within the project Self-Monitoring for Innovation (SeMI). The Swedish agency for innovation, VINNOVA (https://www.vinnova.se/en accessed on 15 May 2021), financed the work in this article through the SAM project, Dnr. 2018-03349.

Available from: 2021-06-30 Created: 2021-06-30 Last updated: 2023-06-12Bibliographically approved

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Nowaczyk, SławomirKnutsson, HåkanCalikus, Ece

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