Pengyuan Shen

Ph.D, associate professor, Shenzhen overseas high-level talent, Shenzhen Nanshan District Piloting Talent, and young editor of Building Simulation journal. I Graduated from the Department of Building Environment and Equipment Engineering of Tongji University and obtained my Ph.D. in Architecture from the University of Pennsylvania in 2018. I joined Tsinghua University Shenzhen International Graduate School in November 2024. I have presided over and participated in a number of national, provincial and municipal funded research projects. At present, I have published over 30 journal papers as first author or corresponding author. My main research directions include building performance and thermal comfort, climate change and urban climate adaptability, and the application of artificial intelligence and machine learning in urban built environment.

Our research group is currently recruiting 2 postdoctoral fellows. At the same time, we recruit 1 doctoral student (mainland, China), 2 international doctoral students, and several master's students every year. Students with computer science, HVAC engineering, geographic information, and architectural and urban planning backgrounds who are interested in creating a future-climate-adaptive human settlement are welcome to join our research group.

Sep. 2012 – May. 2018  University of Pennsylvania, Architecture ,  Ph.D

Sep. 2006 – Jun. 2010.6  Tongji University, Building , Building Environment and Engineering, B.Eng

Nov. 2024 - Present, Associate Professor, Shenzhen International Graduate School, Tsinghua University

Jan. 2022 - Oct. 2024, Associate Professor, Harbin Institute of Technology (Shenzhen)

Sep. 2018 - Dec. 2021, Assistant Professor, Harbin Institute of Technology (Shenzhen)

Jan. 2018 - May 2018, Project Manager, Country Garden Group

Aug. 2011 - Aug. 2012, Research Assistant, School of Mechanical Engineering, Tongji University

July 2010 - July 2012, Assistant Engineer, Shanghai Academy of Building Research

Building Simulation, Youth Editorial Board;

Buildings, Guest Editor

Our team is dedicated to advancing building performance, comfort, and sustainability in response to evolving urban and climate dynamics. My interdisciplinary approach combines architectural design, engineering, and data-driven technologies to enhance building efficiency, resilience, and human-centric environments. At the core of my research, I focus on building energy efficiency, urban climate adaptability, and occupant comfort, often incorporating artificial intelligence and machine learning to optimize performance-driven design strategies.

An important aspect of our team’s work is adapting buildings and urban infrastructure to climate change, particularly through developing predictive models that inform energy retrofits and resilience strategies. By employing and developing advanced modeling and simulation methods and tools, such as microclimate simulations and multi-objective optimization frameworks, my research addresses the potential impacts of future climate conditions and urban heat islands on building energy demands and thermal comfort. Such approaches include coupling climate models with building simulations to optimize energy use and enhance sustainability in high-density urban settings. The contributions extend to improving urban thermal environments, where we investigated microclimate interventions and their impact on outdoor thermal comfort. This research is particularly relevant in tropical and subtropical regions, where urban heat and climate change exacerbates the need for effective cooling strategies. Related projects aim to establish guidelines and decision-support tools that help city planners and architects mitigate the urban heat island effect through intelligent urban design and passive cooling measures.

Another significant area of our research is applying machine learning to performance-driven design. By leveraging data analytics, we developed automated frameworks for evaluating and optimizing various building performance parameters, such as energy consumption, daylighting, and thermal comfort. The models that we developed also contribute to occupant-centric design, addressing how behavioral patterns impact energy use and comfort, with particular emphasis on visual and thermal conditions in built environments like sports facilities and office spaces. My roles in collaborative projects emphasize cross-disciplinary efforts to address environmental challenges, making the involved research pivotal for advancing sustainable architecture and urban resilience in rapidly urbanizing areas.

1.     Zheng Y, Wu J, Zhang H, Lin C, Li Y, Cui X, Shen P*. A novel sun-shading design for indoor visual comfort and energy saving in typical office space in Shenzhen. Energy and Buildings. 2025;328:115083. (WOS Q1, IF: 7.2)

2.     Shen P*, Ji Y, Zhong M. Performance of district energy system under changing climate: A case study of Shenzhen. Applied Energy. 2025;379:124986. (WOS Q1, IF: 11.4)

3.     Li Y, Li L, Cui X, Shen P*. Coupled building simulation and CFD for real-time window and HVAC control in sports space. Journal of Building Engineering. 2024;97:110731. (WOS Q1, IF: 6.7)

4.     Shen P*. Building retrofit optimization considering future climate and decision-making under various mindsets. Journal of Building Engineering. 2024;96:110422. (WOS Q1, IF: 6.7)

5.     Shen P*, Wang H**. Archetype building energy modeling approaches and applications: A review. Renewable and Sustainable Energy Reviews. 2024;199:114478. (WOS Q1, IF: 16.3)

6.     Ji Y, Song J, Shen P*. A data-driven model on human thermophysiological and psychological responses under dynamic solar radiation. Building and Environment. 2024;248:111098. (WOS Q1, IF: 7.4)

7.     Li Y, Li L, Shen P*, Yuan C. An occupant-centric approach for spatio-temporal visual comfort assessment and optimization in daylit sports spaces. Indoor and Built Environment. 2024;33(1):145-166. (WOS Q2, IF: 3.6)

8.     Li Y, Li L, Shen P*, Cui X. Quantifying Occupant Behavior Uncertainty in Spatiotemporal Visual Comfort Assessment of National Fitness Halls: A Machine Learning-Based Co-simulation Framework. Cham: Springer Nature Switzerland; 2023. p. 562-76.

9.     Li Y, Li L, Shen P*. Probability-based visual comfort assessment and optimization in national fitness halls under sports behavior uncertainty. Building and Environment. 2023:110596. (WOS Q1, IF: 7.4)

10.   Shen P*, Wang M, Liu J, Ji Y. Hourly air temperature projection in future urban area by coupling climate change and urban heat island effect. Energy and Buildings. 2023;279:112676. (WOS Q1, IF: 7.2)

11.   Li S, Wang M, Shen P*, Cui X, Bu L, Wei R, et al. Energy Saving and Thermal Comfort Performance of Passive Retrofitting Measures for Traditional Rammed Earth House in Lingnan, China. Buildings. 2022;12(10):1716. (WOS Q2, IF: 3.3)

12.   Du H, Shen P*, Chai WS, Nie D, Shan C, Zhou L*. Perspective and analysis of ammonia-based distributed energy system (DES) for achieving low carbon community in China. iScience. 2022:105120. (WOS Q1, IF: 6.1)

13.   Ji Y, Song J, Shen P*. A review of studies and modeling of solar radiation on human thermal comfort in outdoor environment. Building and Environment. 2022;214:108891. (WOS Q1, IF: 7.4)

14.   Wang M, Shen P*. Investigation of Indoor Asymmetric Thermal Radiation in Tibet Plateau: Case Study of a Typical Office Building. Buildings. 2022;12:129. (WOS Q2, IF: 3.3)

15.   Shi D, Gao Y, Zeng P, Li B, Shen P, Zhuang C*. Climate adaptive optimization of green roofs and natural night ventilation for lifespan energy performance improvement in office buildings. Building and Environment. 2022;223:109505. (WOS Q1, IF: 7.4)

16.   Yu Q, Li S, Shen P*, Ji Y, Wong K-s, Zhang Y. Analysis of DC distribution efficiency based on metered data in a typical Hong Kong office building. Energy Reports. 2022;8:772-9. (WOS Q1, IF: 4.9)

17.   Shen P*, Liu J, Wang M. Fast generation of microclimate weather data for building simulation under heat island using map capturing and clustering technique. Sustainable Cities and Society. 2021;71:102954. (WOS Q1, IF: 10.7)

18.   Shen P*, Wang Z, Ji Y. Exploring potential for residential energy saving in New York using developed lightweight prototypical building models based on survey data in the past decades. Sustainable Cities and Society. 2021;66:102659. (WOS Q1, IF: 10.7)

19.   Shen P, Yang B*. Projecting Texas energy use for residential sector under future climate and urbanization scenarios: A bottom-up method based on twenty-year regional energy use data. Energy. 2020;193:116694. (WOS Q1, IF: 8.9)

20.   Shen P*, Wang Z. How neighborhood form influences building energy use in winter design condition: Case study of Chicago using CFD coupled simulation. Journal of Cleaner Production. 2020;261:121094. (WOS Q1, IF: 11.1)

21.   Shen P*;Dai Mingkun; Xu Peng; Dong Wei, Building heating and cooling load under different neighbourhood forms: Assessing the effect of external convective heat transfer, Energy, 2019, 173: 75~91  (WOS Q1, IF: 8.9)

22.   Shen P*;Braham William; Yi Yunkyu; Eaton Eric, Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit, Energy, 2019, 172: 892~912 (WOS Q1, IF: 8.9)

23.   Shen P*;Braham William; Yi Yunkyu, The feasibility and importance of considering climate change impacts in building retrofit analysis, Applied Energy, 2019, 233-234: 254~270 (WOS Q1, IF: 11.4)

24.   Shen P*; Braham William; Yi Yunkyu, Development of a lightweight building simulation tool using simplified zone thermal coupling for fast parametric study, Applied Energy, 2018, 223: 188~214  (WOS Q1, IF: 11.4)

25.   Shen P*, Impacts of climate change on US building energy use by using downscaled hourly future weather data, Energy and Buildings, 2017, 134: 61~70 (WOS Q1, IF: 7.2)

26.   Shen P*; Lior Noam, Vulnerability to climate change impacts of present renewable energy systems designed for achieving net-zero energy buildings, Energy, 2016, 114: 1288~1305 (WOS Q1, IF: 8.9)

27.   Shen P*; Lukes Jennifer R, Impact of global warming on performance of ground source heat pumps in US climate zones, Energy Conversion and Management, 2015, 101: 632~643 (WOS Q1, IF: 11.53)