Journal of Zhejiang University SCIENCE  A

Accepted manuscript available online (unedited version)


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


Author(s):  Xu ZHOU1, 2*, Songjie WANG1*, Yanhao FENG3, Xueru LIN1, Wenxuan GUO1, Nan ZHANG1, Lingkai ZHU4, Wei ZHONG1, 5, Zitao YUsup>1, 5, Xingtao TIAN6

Affiliation(s):  1College of Energy Engineering, Zhejiang University, Hangzhou 310027, China 2Jinan Heating Group Co., LTD, Jinan 250011, China 3Solution Management, Zhejiang Engipower LTD, Hangzhou 311121, China 4State Grid Shandong Electric Power Research Institute, Jinan 250003, China 5Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, Zhejiang University, Hangzhou 310027, China 6Key Laboratory of Cleaner Intelligent Control on Coal & Electricity, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China

Corresponding email(s):  Xueru LIN, linxueru@zju.edu.cn Yanhao FENG, yanhaofeng@zju.edu.cn

Key Words:  District heating network; Artificial intelligence; Lifecycle; Digital thread


Share this article to： More <<< Previous Paper|

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,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2600093

@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",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/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
%P
%@ 1673-565X
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/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
SP -
EP -
%@ 1673-565X
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Full Text:  <10>

CLC number: 

On-line Access: 2026-05-11

Received: 2026-02-10

Revision Accepted: 2026-05-02

Crosschecked: 0000-00-00

Cited: 0

Clicked: 17

Citations:  Bibtex RefMan EndNote GB/T7714

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2026 Journal of Zhejiang University-SCIENCE