A smart approach to maintenance of sustainable district heating systems: Techniques, challenges, and future directions

Currently, district heating systems are essential for the effective distribution of energy derived from renewable sources, such as geothermal and solar thermal energy, to extensive regions, including residential and urban communities. However, faults can affect the system's efficiency and lead to energy waste and significant economic losses. DHS's dependability and effectiveness are even more important as the shift to renewable energy sources accelerates, especially under the Net Zero Emissions by 2025 Scenario. Leakage can be one of the critical faults in the system, including the loss of energy, impact on the environment, challenging stability, and damage to the system equipment. Therefore, leak detection must be quick and precise to avoid system issues and costs. This review study provides a comprehensive review of leakage detection methods, highlighting their evolution, advantages, limitations, and prospects. Traditional model-based approaches are analyzed alongside data-driven techniques and advanced methods such as Unmanned Airborne Infrared Thermography (UAIT). The review also discusses challenges like network complexity, sensor limitations, and the trade-offs between cost, accuracy, and efficiency of different methods. While many studies demonstrate promising results, their reliance on simulated data rather than real-world validation remains a key constraint. The article recommends integrating multiple methods to improve system monitoring and predictive maintenance. It also highlights future directions involving proposed algorithms and models based on state-space nonlinear methods, which are well-suited for complex systems such as DHS. This approach will be helpful in achieving high accuracy and faster detection within the system. A view is held regarding the potential for improving the monitoring and predictive maintenance system while considering the sustainable use of renewable energies for district heating and discussing the benefits and drawbacks of those various detection methodologies. • Comprehensive review of leakage detection in district heating systems (DHS). • Analyze model-based, data-driven, and UAIT methods for leakage detection. • Provide comprehensive challenges in accuracy, cost, and system complexity. • Highlights advancement in sensor technology and machine learning applications.