张冲，男，博士，现任华中科技大学建筑与城市规划学院 建筑系 副研究员，华中科技大学博士，历任香港理工大学Postdoc Fellow博士后、Research Fellow研究员，入选2024年度“武汉英才”计划、2019年度国家人力资源与社会保障部“香江学者计划”。
主要研究兴趣包括：绿色建筑、性能导向智能化设计、建筑物理环境控制与优化、智能建筑技术与数据驱动建模等。迄今在建筑技术、建筑物理、建筑节能领域已发表学术论文50余篇，在国际知名期刊Applied Energy、Energy and Buildings, Energy, Sustainable Cities and Society发表SCI论文30余篇，其中JCR一区论文26篇，2篇论文入选ESI高被引论文，相关研究成果被Joule（影响因子38.6）等国际权威期刊论文正面引用，单篇论文最高被引188次。授权与申请发明专利、实用新型专利与软件著作权8项。
作为项目负责人，主持国家自然科学基金青年项目、香江学者计划项目、中国博士后科学基金面上一等资助项目、企业技术研发项目等。作为项目骨干参与“十三五”和“十四五”国家重点研发计划项目，作为主要完成人参与国家自然科学基金项目3项。现为国家自然科学基金通讯评审专家，国际建筑性能模拟协会会员（IBPSA member），中国仿真学会建筑仿真专业委员会委员，暖通空调产业技术创新联盟智能化专业委员会委员，担任国际SCI期刊Buildings、Energies客座编辑Guest Editor，担任Applied Energy, Energy and Buildings, Building and Environment, Sustainable Cities and Society等10余个国际知名SCI期刊、及中文核心期刊《建筑科学》的审稿专家。
欢迎保送或报考本人研究生，也欢迎对本人科研方向感兴趣的本科生参与相关项目研究、开展大学生创新项目工作、并指导同学发表SCI论文，请直接与我联系！（Email: zhangchong@hust.edu.cn）
个人学术主页：
Google Scholar: https://scholar.google.com.hk/citations?hl=en&user=SpzNsMkAAAAJ
Researchgate: https://www.researchgate.net/profile/Chong-Zhang-10?ev=hdr_xprf
代表性科研项目：
[1] 国家自然科学基金青年项目, 基于主动隔热与排风热回收的建筑外窗耦合动态传热模型及实验研究(51808239), 2019.01-2021.12, 已结题, 项目负责人
[2] 香江学者计划, 为智能节能建筑研发大数据驱动的模拟和优化方法(XJ2019044), 2020.06-2022.06,已结题, 项目负责人
[3] 中国博士后科学基金一等资助项目, 建筑外窗排风隔热机制的设计理论与气候适应性研究(2018M640702), 2018.12-2019.12, 已结题, 项目负责人
[4] 华中科技大学启动经费, 融合可再生能源的建筑人居环境营造技术研究, 2023.02-2026.06, 在研, 项目负责人
[5] 中信集团重大科研项目，碳达峰与碳中和背景下夏热冬冷地区超低能耗建筑关键技术体系研究，2013.07-2014.12，已结题，子任务负责人
[6] 企业技术服务项目，考虑多目标的建筑综合能源系统电力运行优化控制算法研发项目，2024.05-2024.12，已结题，项目负责人
[7] 企业技术服务项目，夏热冬冷地区适宜的建筑围护结构技术体系，2024.06-2025.06，在研，子任务负责人
[8] 国家重点研发计划项目，医疗建筑平急结合关键技术研究与应用（2023YF3806503），2023.12-2027.05，在研，课题骨干。
[9] 国家重点研发计划项目, 高密度城市中建筑综合能源系统的智能管理技术研发(2021YFE0107400), 2021.07-2024.06, 612万元, 已结题，项目骨干。
[10] 香港创新及科技支援计划项目, Smart Data-Driven Building Management Framework for Environmental and Sustainability Applications(ITP/002/22LP), 2022.03-2024.02, 730万港币, 已结题, 项目经理
[11] 国家自然科学基金面上项目, 建筑外墙气流外渗隔热机制研究(51378231), 2014.01-2017.12, 80万元, 已结题，主要参与人
[12] 国家自然科学基金面上项目, 外层定型相变与内嵌管式通风屋面的主动隔热与蓄冷机制研究(51778255), 2018.01-2021.12, 60万元, 已结题, 主要参与人
[13] 国家自然科学基金青年项目, 基于随机不确定性与非概率不确定性量化分析的中央空调系统性能预测与优化配置方法研究(51808238), 2019.01-2021.12, 26万元, 已结题, 主要参与人
国际SCI期刊论文：
[1] C Zhang, W Gang, X Xu, et al. Modelling, experimental test, and design of an active air permeable wall by utilizing the low-grade exhaust air. Applied Energy, 2019, 240: 730-743. (IF=10.1，SCI一区)
[2] C Zhang, J Wang, L Li, et al. Dynamic thermal performance and parametric analysis of a heat recovery building envelope based on air-permeable porous materials. Energy, 2019, 189: 116361. (IF=9，SCI一区)
[3] C Zhang, W Gang, J Wang, et al. Numerical and experimental study on the thermal performance improvement of a triple glazed window by utilizing low-grade exhaust air. Energy, 2019, 167: 1132-1143. (IF=9，SCI一区)
[4] C Zhang, X Xue, Q Du, et al. Study on the performance of distributed energy systems based on historical loads considering parameter uncertainties for decision making. Energy, 2019, 176: 778-791. (IF=9，SCI一区)
[5] J Yu, H Yang, J Zhao, C Zhang*, et al. Study on thermal performance of dynamic insulation roof integrated with phase change material. Energy and Buildings, 2024, 303: 113832. (IF=6.6，SCI一区)
[6] C Zhang, C Cui, Y Zhang, et al. A review of renewable energy assessment methods in green building and green neighborhood rating systems. Energy and Buildings, 2019, 195: 68-81. (IF=6.6，SCI一区)
[7] L Li, C Zhang*, W Gang, et al. Frequency thermal characteristic and parametric study of multi-functional heat recovery building envelope: Modelling and experimental validation. Energy and Buildings, 2021, 253: 111541. (IF=6.6，SCI一区)
[8] J Wang, Q Du, C Zhang*, et al. Mechanism and preliminary performance analysis of exhaust air insulation for building envelope wall. Energy and Buildings, 2018, 173: 516-529. (IF=6.6，SCI一区)
[9] C Zhang, X Xu, J Yu, et al. Condensation risk-based applicability analysis and design of a dynamic thermal insulation window with ventilated airflow in different climates. Journal of Building Engineering, 2024, 86: 108913. (IF=6.7，SCI一区)
[10] C Zhang*, F Xiao, J Wang. Design optimization of multi-functional building envelope for thermal insulation and exhaust air heat recovery in different climates. Journal of Building Engineering, 2021, 43: 103151. (IF=6.7，SCI一区)
[11] L Li, C Zhang*, X Xu, et al. Simulation study of a dual-cavity window with gravity-driven cooling mechanism. Building Simulation, 2022, 15: 1339–1352. (IF=6.1，SCI一区)
[12] C Zhang, W Gang, J Wang, et al. Experimental investigation and dynamic modeling of a triple-glazed exhaust air window with built-in venetian blinds in the cooling season. Applied Thermal Engineering, 2018, 140: 73-85.(IF=6.1，SCI一区)
[13] C Zhang, J Wang, X Xu, et al. Modeling and thermal performance evaluation of a switchable triple glazing exhaust air window. Applied Thermal Engineering, 2016, 92: 8-17. (IF=6.1，SCI一区)
[14] C Zhang*, J Wang. Determining the critical insulation thickness of breathing wall: Analytical model, key parameters, and design. Case Studies in Thermal Engineering, 2021, 27: 101326. (IF=6.4，SCI一区)
[15] C Zhang*, J Wang, L Li, et al. Condensation risk of exhaust air heat recovery window system: Assessment, key parameters, and prevention measure. Case Studies in Thermal Engineering, 2021, 24: 100830. (IF=6.4，SCI一区)[16] C Zhang, Z Yu, Q Zhu, et al. Air-permeable building envelopes for building ventilation and heat recovery: Research progress and future perspectives. Buildings 14 (2023) 42. (IF=3.8，SCI二区)
[17] C Zhang, J Wang, L Li, et al. Utilization of earth-to-air heat exchanger to pre-cool/heat the ventilation air and its annual energy performance evaluation: A case study. Sustainability, 2020, 12: 8330. (IF=3.9，SCI二区)
[18] J Guo, C Zhang*. Utilization of window system as exhaust air heat recovery device and its energy performance evaluation: A comparative study. Energies, 2022, 15: 3116. (IF=3.2，SCI三区)
[19] X Jin, F Xiao, C Zhang, et al. Semi-supervised learning based framework for urban level building electricity consumption prediction. Applied Energy, 2022, 328: 120210. (IF=10.1，SCI一区)
[20] X Jin, F Xiao, C Zhang, et al. GEIN: An interpretable benchmarking framework towards all building types based on machine learning. Energy and Buildings, 2022, 260: 111909. (IF=6.6，SCI一区)
[21] A Li, F Xiao, C Zhang, C Fan. Attention-based interpretable neural network for building cooling load prediction. Applied Energy, 2021, 299: 117238. (IF=10.1，SCI一区)
[22] Y Yang, J Yuan, Z Xiao, H Yi, C Zhang, W Gang, H Hu. Energy consumption characteristics and adaptive electricity pricing strategies for college dormitories based on historical monitored data. Energy and Buildings, 2021, 245: 111041. (IF=6.6，SCI一区)
[23] J Yuan, C Cui, Z Xiao, C Zhang, W Gang. Performance analysis of thermal energy storage in distributed energy system under different load profiles. Energy Conversion and Management, 2020, 208: 112596. (IF=10.4，SCI一区
[24] J Yuan, Z Xiao, C Zhang, et al. A control strategy for distributed energy system considering the state of thermal energy storage. Sustainable Cities and Society, 2020, 63: 102492. (IF=11.7，SCI一区)
[25] J Gao, J Kang, C Zhang, et al. Energy performance and operation characteristics of distributed energy systems with district cooling systems in subtropical areas under different control strategies. Energy, 2018, 153: 849-860. (IF=9，SCI一区)
[26] J Yuan, W Gang, F Xiao, C Zhang, Y. Zhang. Two-level collaborative demand-side management for regional distributed energy system considering carbon emission quotas. Journal of Cleaner Production, 2024, 434: 140095. (IF=11.1，SCI一区)
[27] L Su, M Liu, Z Ling, W Gang, C Zhang, Y Zhang X Hao. A filling method based on K-singular value decomposition (K-SVD) for missing and abnormal energy consumption data of buildings. Buildings, 2024, 14: 696. (IF=3.8，SCI二区)
[28] A Li, C Zhang, F Xiao, et al. Large-scale comparison and demonstration of continual learning for adaptive data-driven building energy prediction. Applied Energy, 2023, 347: 121481. (IF=11.2)
[29] X Jin, C Zhang, F Xiao, et al. A review and reflection on open datasets of city-level building energy use and their applications. Energy and Buildings, 2023, 285: 112911. (IF=6.6，SCI一区)
[30] H Zhang, F Xiao, C Zhang, R Li, A multi-agent system based coordinated multi-objective optimal load scheduling strategy using marginal emission factors for building cluster demand response. Energy and Buildings, 2023, 281: 112765. (IF=6.6，SCI一区)
（更新于2025年3月14日）