Clustering district heating customers based on load profiles

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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.

Keywords

clustering / district heating / customer profiling

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