Overview of Data-Driven Methods for District Heating Systems Diagnosis

District heating systems are essential for efficient and sustainable urban energy management, offering significant energy savings and environmental benefits. This paper presents some key data-driven methodologies, including advanced data analytics, machine learning, artificial neural networks and other modern methods to evaluate and optimize the design and operation of district heating networks. Several application areas are discussed: demand forecasting, design optimization of the network, fault detection and diagnosis. Recommendations regarding the use of Big Data and AI-driven insights combined with traditional thermal-hydraulic analysis to address challenges such as load variability, energy losses, and operational inefficiencies are formulated. Key challenges and limitations are highlighted, such as data quality and availability, algorithm choice, scalability, etc. The paper aims to provide insights into the potential of data-driven methods to transform classic district heating systems into smarter and sustainable systems towards wide implementation of the 4GDH.