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A data-driven approach for discovering heat load patterns in district heating
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-6249-4144
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
Halmstad University, School of Business, Engineering and Science. Öresundskraft, Helsingborg, Sweden.
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2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 252, article id 113409Article in journal (Refereed) Published
Abstract [en]

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers’ heat-use habits. © 2019 Calikus et al. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Oxford: Elsevier, 2019. Vol. 252, article id 113409
Keywords [en]
District heating, Energy efficiency, Heat load patterns, Clustering, Abnormal heat use
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-40907DOI: 10.1016/j.apenergy.2019.113409Scopus ID: 2-s2.0-85066961984OAI: oai:DiVA.org:hh-40907DiVA, id: diva2:1369639
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Knowledge Foundation, 20160103Available from: 2019-11-12 Created: 2019-11-12 Last updated: 2019-11-13

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Calikus, EceNowaczyk, SławomirPinheiro Sant'Anna, AnitaGadd, HenrikWerner, Sven

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Calikus, EceNowaczyk, SławomirPinheiro Sant'Anna, AnitaGadd, HenrikWerner, Sven
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CAISR - Center for Applied Intelligent Systems ResearchSchool of Business, Engineering and ScienceThe Rydberg Laboratory for Applied Sciences (RLAS)
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Applied Energy
Other Electrical Engineering, Electronic Engineering, Information Engineering

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