A data-driven approach for discovering heat load patterns in district heating
This work addresses the need for better understanding of customer heat usage patterns for district heating operations, though it is incremental as it applies existing clustering techniques to a new domain.
The authors tackled the problem of scarce knowledge about customer heat load behaviors in district heating by proposing a data-driven clustering method, enabling the first large-scale analysis of 1222 buildings in Sweden, which used 1540 TJ of heat in 2016.
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.