  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.
