2019/01/31 by Ece Calikus, Slawomir Nowaczyk, Sławomir Nowaczyk +5 · 90 citations
Computer Science · Engineering · Environmental Science · Mathematics · #Atmospheric and Environmental Gas Dynamics #Building Energy and Comfort Optimization #Computer science #Data mining #Data science #Engineering #Environmental science #Geography #Heat load #Integrated Energy Systems Optimization #Mechanical engineering #Scale (ratio) #Space (punctuation) #Work (physics) #cs.CE #cs.LG #stat.ML
paper · pdf · doi:10.1016/j.apenergy.2019.113409
published in Applied Energy 252, 113409 (Elsevier BV)
openalex publication_date 2019/06/10 · arxiv created 2019/09/18 · arxiv updated 2019/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.