# A data-driven approach for discovering heat load patterns in district heating

arXiv: https://arxiv.org/abs/1901.04863 · HTML: https://mlsanity.com/paper/1901.04863/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering](https://mlsanity.com/paper/2103.08796/) (2021)
- [Enabling Predictive Maintenance in District Heating Substations: A Labelled Dataset and Fault Detection Evaluation Framework based on Service Data](https://mlsanity.com/paper/2511.14791/) (2025)
- [SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations](https://mlsanity.com/paper/2408.14499/) (2024)
- [A multi-dimensional unsupervised machine learning framework for clustering residential heat load profiles](https://mlsanity.com/paper/2411.16701/) (2024)
- [MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation](https://mlsanity.com/paper/2607.29177/) (2026)

## Abstract (arXiv)

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.