Clustering district heating customers based on load profiles

Vili Lavikainen; Pasi Fränti; Vili Lavikainen; Pasi Fränti

doi:10.3934/aci.2024016

Applied Computing and Intelligence

2024, Volume 4, Issue 2: 269-281. doi: 10.3934/aci.2024016

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Research article

Clustering district heating customers based on load profiles

Vili Lavikainen ¹,
Pasi Fränti ^{1,2
,
,}

1.
School of Computing, University of Eastern Finland, Joensuu, Finland
2.
School of Data Science, Chinese University of Hong Kong, Shenzhen, China

Received: 18 December 2024 Revised: 19 December 2024 Accepted: 19 December 2024 Published: 26 December 2024

Intelligent district heating control requires knowing the customers' past behavior and predicting their future needs. This can reduce peak energy use, optimizing energy production, accurate billing, and reducing fraud. Clustering has been used for analyzing large-scale building operational data and recognizing consumption profiles. In this work, we analyze the heat consumption profiles of district heat customers in Kuopio, Finland. We constructed two consumption profiles of their average hourly use: one for weekdays, and one for weekends. Clustering is then used to construct four consumption profiles. These profiles can be used for intelligent control, prediction of future use, and to recognize abnormal use behavior. The latter can be the first indication of a problem like heat leaking, which can prevent possible water damage.
- clustering,
- district heating,
- customer profiling
Citation: Vili Lavikainen, Pasi Fränti. Clustering district heating customers based on load profiles[J]. Applied Computing and Intelligence, 2024, 4(2): 269-281. doi: 10.3934/aci.2024016

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Abstract

Intelligent district heating control requires knowing the customers' past behavior and predicting their future needs. This can reduce peak energy use, optimizing energy production, accurate billing, and reducing fraud. Clustering has been used for analyzing large-scale building operational data and recognizing consumption profiles. In this work, we analyze the heat consumption profiles of district heat customers in Kuopio, Finland. We constructed two consumption profiles of their average hourly use: one for weekdays, and one for weekends. Clustering is then used to construct four consumption profiles. These profiles can be used for intelligent control, prediction of future use, and to recognize abnormal use behavior. The latter can be the first indication of a problem like heat leaking, which can prevent possible water damage.

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