SHI Xing

Professor

20101@tongji.edu.cn

Room 217, Building A, CAUP, Tongji Univ.

Research Profile 

Green building performance, Urban building energy modelling, Urban climate

Research Profile 

SHI Xing received his Bachelor degree from Tongji University in 1998. He then studied at the Pennsylvania State University and received his Ph.D. degree in 2005. From 2005 to 2008, he worked at Walter P Moore in Houston as a consulting engineer. In early 2008, he joined the School of Architecture at Southeast University. Since September 2020, he has been a full professor in the College of Architecture and Urban Planning at Tongji University and the Chair of the Key Laboratory of Ecology and Energy-saving Study of Dense Habitat, Ministry of Education, China.

SHI Xing’s research interests include green building performance and optimization design, building energy efficiency, urban physics, and urban energy modeling. He is the PI of more than 20 research projects, including those funded by the Natural Science Foundation of China, the Ministry of Science and Technology of China, the Ministry of Education of China, etc. The total funding of these research projects exceeds RMB 15 million. In addition to research, he has extensive design and consulting experiences. One of his recent works is leading the project of green design and sustainable technology integration of the National Cultural Center in Beijing, China.

SHI Xing has published more than 150 papers on journals and conferences. These journals include Applied Energy, Energy, Energy and Buildings, Building and Environment, Automation in Construction, Landscape and Urban Planning, Sustainable Cities and Society, Renewable and Sustainable Energy Reviews, etc. He is the author of five books and holds more than 20 patents.

SHI Xing is the vice president of the Institute of Building Physics of China. He serves on more than 10 editorial boards and technical committees. He has won multiple awards, both domestically and internationally.

Current Courses

1. Urban climate

2. Design studio: performance of “city-building” system supported by digital technology

3. Green building: Its Science and Technology

4. Urban physics

Grants and Awards

1. Performance-based multi-objective residential complex design driven by simulation and algorithms, National Natural Science Foundation, 2011-2013.

2. Building energy efficient design and technical tool prototype driven by optimization algorithms and intelligent knowledge database, National Natural Science Foundation, 2017-2020.

3. Performance gap of green buildings, Ministry of Science and Technology, 2016-2020.

4. Urban heat island and its mitigation in urban design, Bureau of Science and Technology, Jiangsu Province, 2014-2016.

5. Post-evaluation of green cities, Bureau of Housing and Urban-rural Development, Jiangsu Province, 2020-2022.

6. 30 Years of transformation of urban surfaces and heat island in Nanjing, Best Paper Award, The 10^th International Symposium on City Planning and Environmental Management in Asian Countries, 2016.

7. Ineffective of optimization algorithms in building energy optimization and possible causes, Key Scientific Article, Advances in Engineering.

8. Research on the efficacy of optimization algorithms used in building energy efficient design optimization, Excellent Doctoral Thesis, Advisor, Jiangsu Province, 2022.

9. Theoretical study and application of spatial genes in urban development, First Prize of Huaxia Science and Technology Award, 2018.

10. Holistic optimization of built environment in historical cities and districts, Second Prize of Science and Technology Award, Ministry of Education, 2015.

Publications (2020-2022):

[1]C. Wang, Y. Wu, X. Shi, Y. Li, S. Zhu, X. Jin, and X. Zhou. Dynamic occupant density models of commercial buildings for urban energy simulation. Building and Environment, 2020, 169: 106549.

[2]S. Du, Y. Li, C. Wang, Z. Tian, Y. Lu, S. Zhu, and X. Shi. A Cross-Scale Analysis of the Correlation between Daytime Air Temperature and Heterogeneous Urban Spaces. Sustainability, 2020, 12(18): 7663.

[3]Y. Li, C. Wang, S. Zhu, J. Yang, S. Wei, X. Zhang, and X. Shi. A Comparison of Various Bottom-Up Urban Energy Simulation Methods Using a Case Study in Hangzhou, China. Energies, 2020, 13(18): 4781.

[4]Y. Li, X. Zhang, S. Zhu, X. Wang, Y. Lu, S. Du, and X. Shi. Transformation of Urban Surfaces and Heat Islands in Nanjing during 1984–2018. Sustainability, 2020, 12(16): 6521.

[5]J. Ren, X. Zhou, J. An, D. Yan, X. Shi, X. Jin, and S. Zheng. Comparative analysis of window operating behavior in three different open-plan offices in Nanjing. Energy and Built Environment, 2021, 2(2): 175-87.

[6]Z. Tian, S. Wei, and X. Shi. Developing data-driven models for energy-efficient heating design in office buildings. Journal of Building Engineering, 2020, 32: 101778.

[7]X. Wang, X. Jin, Y. Yin, X. Wang, X. Shi, and X. Zhou. Study on non-isothermal moisture transfer characteristics of hygroscopic building materials: From parameter characterization to model analysis. Energy, 2020, 212: 118788.

[8]X. Zhou, T. Liu, D. Yan, X. Shi, and X. Jin. An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces. Building Simulation, 2020, 14(2): 301-15.

[9]D. Zhuang, X. Zhang, Y. Lu, C. Wang, X. Jin, X. Zhou, and X. Shi. A performance data integrated BIM framework for building life-cycle energy efficiency and environmental optimization design. Automation in Construction, 2021, 127: 103712.

[10]Z. Tian, X. Shi, and S. Hong. Exploring data-driven building energy-efficient design of envelopes based on their quantified impacts. Journal of Building Engineering, 2021, 42: 103018.

[11]Z. Tian, X. Zhang, S. Wei, S. Du, and X. Shi. A review of data-driven building performance analysis and design on big on-site building performance data. Journal of Building Engineering, 2021, 41: 102706.

[12]C. Wang, X. Shi, M. Wang, and R. Liu. Determination of Urban Building Heights using Artificial Intelligence. 17th International Conference on Computational Urban Planning and Urban Management. Helsinki, Finland (Online). 2021

[13]C. Wang, S. Wei, S. Du, D. Zhuang, Y. Li, X. Shi, X. Jin, and X. Zhou. A systematic method to develop three dimensional geometry models of buildings for urban building energy modeling. Sustainable Cities and Society, 2021, 71: 102998.

[14]X. Wang, X. Jin, Y. Yin, X. Shi, and X. Zhou. A transient heat and moisture transfer model for building materials based on phase change criterion under isothermal and non-isothermal conditions. Energy, 2021, 224: 120112.

[15]X. Zhang, A. Wang, Z. Tian, Y. Li, S. Zhu, X. Shi, X. Jin, X. Zhou, and S. Wei. Methodology for developing economically efficient strategies for net zero energy buildings: A case study of a prototype building in the Yangtze River Delta, China. Journal of Cleaner Production, 2021, 320: 128849.

[16]X. Zhou, J. Ren, J. An, D. Yan, X. Shi, and X. Jin. Predicting open-plan office window operating behavior using the random forest algorithm. Journal of Building Engineering, 2021, 42: 102514.

[17]X. Zhou, S. Tian, J. An, J. Yang, Y. Zhou, D. Yan, J. Wu, X. Shi, and X. Jin. Comparison of different machine learning algorithms for predicting air-conditioning operating behavior in open-plan offices. Energy and Buildings, 2021, 251: 111347.

[18]S. Zhu, S. Du, Y. Li, S. Wei, X. Jin, X. Zhou, and X. Shi. A 3D spatiotemporal morphological database for urban green infrastructure and its applications. Urban Forestry & Urban Greening, 2021, 58: 126935.

[19]S. Zhu, Y. Li, C. Wang, X. Zhang, and X. Shi. The impact of the spatio-temporal morphology of urban green infrastructure on urban building energy consumption: A case study in the hot-summer-cold-winter climate. Journal of Physics: Conference Series, 2021, 2069(1): 012059.

[20]S. Du, X. Zhang, X. Jin, X. Zhou, and X. Shi. A review of multi-scale modelling, assessment, and improvement methods of the urban thermal and wind environment. Building and Environment, 2022, 213: 108860.

[21]D. Song, X. Zhang, X. Zhou, X. Shi, and X. Jin. Influences of wind direction on the cooling effects of mountain vegetation in urban area. Building and Environment, 2022, 209: 108663.

[22]C. Wang, M. Ferrando, F. Causone, X. Jin, X. Zhou, and X. Shi. Data acquisition for urban building energy modeling: A review. Building and Environment, 2022, 217: 109056.

[23]Y. Zhang, X. Wang, X. Jin, A. Wang, X. Shi, X. Zhou, and X. Fu. Moisture transfer characteristics of the wall with phase change material. Journal of Thermal Analysis and Calorimetry, 2022, 147(3): 2679-88.

[24]S. Zhu, Y. Yang, Y. Yan, F. Causone, X. Jin, X. Zhou, and X. Shi. An evidence-based framework for designing urban green infrastructure morphology to reduce urban building energy use in a hot-humid climate. Building and Environment, 2022, 219: 109181.

[25]D. Zhuang, T. Wang, V.J.L. Gan, X. Zhao, Y. Yang, and X. Shi. Supervised learning-based assessment of office layout satisfaction in academic buildings. Building and Environment, 2022, 216: 109032.

[26]Z. Tian and X. Shi. Proposing energy performance indicators to identify energy-wasting operations on big time-series data. Energy and Buildings, 2022, 269: 112244.

[27]C. Wang, M. Ferrando, F. Causone, X. Jin, X. Zhou, and X. Shi. An innovative method to predict the thermal parameters of construction assemblies for urban building energy models. Building and Environment, 2022, 224: 109541.

[28]X. Zhou, J. Ren, C. Gui, J. An, C. Xiao, Y. Tao, X. Shi, X. Jin, and D. Yan. Generation and verification of vertical meteorological data for building energy simulation from a 325-meter Beijing meteorological tower. Energy and Buildings, 2022, 262: 111992.

[29]S. Zhu, Y. Li, S. Wei, C. Wang, X. Zhang, X. Jin, X. Zhou, and X. Shi. The impact of urban vegetation morphology on urban building energy consumption during summer and winter seasons in Nanjing, China. Landscape and Urban Planning, 2022, 228: 104576.

[30]D. Zhuang, and X. Shi. Building Information Modelling based Transparent Envelope Optimization Considering Environmental Quality, Energy and Cost. CAADRIA 2022. Sydney, Australia. 2022

[31]X. Zhang, and X. Shi. A method for road network generation based on tensor field and multi-agent. 7th SDSC. Sydney, Australia. 2022