Integrating artificial intelligence in the lifecycle evolution of district heating networks: challenges and opportunities

The evolution of next-generation district heating networks toward higher efficiency and sustainability is constrained by persistent challenges in supply–demand coordination and lifecycle optimization. This paper provides a comprehensive review of artificial intelligence (AI) integration across four key lifecycle stages of heating networks: planning and design, construction and renewal, operation and control, as well as maintenance and fault diagnosis. Critical research gaps are identified, including application fragmentation and persistent data silos. The literature has demonstrated that AI-based approaches can deliver significant performance improvements across multiple lifecycle stages. In the planning phase, AI can improve design computational efficiency, in some cases by up to an order of magnitude. In the construction phase, AI-enhanced management has been shown to accelerate project timelines while reducing costs. For operational control, deep learning models can reduce thermal load forecasting errors by more than half. In the maintenance phase, AI enables multiday early fault warnings with localization accuracies exceeding 95% in representative studies. To address the limitations of fragmented applications, a digital thread framework is proposed to enable cross-lifecycle data continuity and integration of decision-making across engineering phase. Finally, future research directions are outlined, emphasizing adaptive AI frameworks aligned with urban evolution, the integration of digital twins with intelligent optimization agents, and the development of interpretable and transferable AI models.