
Xu ZHOU1,2*, Songjie WANG1*, Yanhao FENG3, Xueru LIN1, Wenxuan GUO1, Nan ZHANG1, Lingkai ZHU4, Wei ZHONG1,5, Zitao YUsup>1,5, Xingtao TIAN6. Integrating artificial intelligence in the lifecycle evolution of district heating networks: challenges and opportunities[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Integrating artificial intelligence in the lifecycle evolution of district heating networks: challenges and opportunities",
author="Xu ZHOU1,2*, Songjie WANG1*, Yanhao FENG3, Xueru LIN1, Wenxuan GUO1, Nan ZHANG1, Lingkai ZHU4, Wei ZHONG1,5, Zitao YUsup>1,5, Xingtao TIAN6",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2600093"
}
%0 Journal Article
%T Integrating artificial intelligence in the lifecycle evolution of district heating networks: challenges and opportunities
%A Xu ZHOU1
%A 2*
%A Songjie WANG1*
%A Yanhao FENG3
%A Xueru LIN1
%A Wenxuan GUO1
%A Nan ZHANG1
%A Lingkai ZHU4
%A Wei ZHONG1
%A 5
%A Zitao YUsup>1
%A 5
%A Xingtao TIAN6
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2600093
TY - JOUR
T1 - Integrating artificial intelligence in the lifecycle evolution of district heating networks: challenges and opportunities
A1 - Xu ZHOU1
A1 - 2*
A1 - Songjie WANG1*
A1 - Yanhao FENG3
A1 - Xueru LIN1
A1 - Wenxuan GUO1
A1 - Nan ZHANG1
A1 - Lingkai ZHU4
A1 - Wei ZHONG1
A1 - 5
A1 - Zitao YUsup>1
A1 - 5
A1 - Xingtao TIAN6
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP - 0
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2600093
Abstract: 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, and maintenance and fault diagnosis. Critical research gaps are identified, including application fragmentation and persistent data silos. Existing 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 stage, AI enables multi-day early fault warnings with localization accuracies exceeding 95% in representative studies. To address the limitation of fragmented applications, a digital thread framework is proposed to enable cross-lifecycle data continuity and integration of decision-making across engineering stages. Finally, future research directions are outlined, emphasizing adaptive AI frameworks aligned with urban evolution, integration of digital twins with intelligent optimization agents, and the development of interpretable and transferable AI models.
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On-line Access: 2026-05-11
Received: 2026-02-10
Revision Accepted: 2026-05-02
Crosschecked: 0000-00-00
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