Prof. Zhe Tian/
Gender : Male
Email : email@example.com
2005 : PhD in Building technique from Tianjin University, China.
1999 : MSc in HVAC from Tianjin University, China.
1996 : BSc in HVAC from Southwest Jiaotong University, China.
2013 –now : Professor, School of Environmental Science and Engineering, Tianjin University, China
2006 –2013 : Associate Professor, School of Environmental Science and Engineering, Tianjin University, China
2006-2007 : Visiting Scholar, RWTH, Germany
Optimization of building energy system planning and operation
Carry out the theoretical method research of building planning load prediction and building energy system optimization design decision. Funded by the National Natural Science Foundation of China and the National Key Research and Development Program, we independently developed the "Building energy system optimization Planning and design software" of BS architecture. The research group is working on the theoretical direction of building energy system uncertainty planning, and the results will support the systematic robust design of integrated building and renewable energy and the optimization design of near-zero carbon and zero energy buildings.
Research work about operation has been carried out on the theory and algorithm of building load day-ahead prediction, daytime multi-step prediction and single-step prediction, and a relatively complete technical system of building operating load prediction has been formed. In addition, based on the new generation of engineering modelling language Modelica, the co-simulation research of building performance and energy system is carried out, and the standardized simulation model library of building and energy system is constructed to support the efficient operation of building energy system and the flexible operation of friendly interaction with power grid. Funded by the National Natural Science Foundation of China, Postdoctoral Foundation and the 13th Five-Year National Key Research and Development Program, the company has developed the simulation technology of building integrated energy system and successively undertaken the technical development projects entrusted by Xiongan New Area and the State Grid.
- National Natural Science Foundation of China, General Project, Extraction of component flux of building cooling load based on time series data disaggregation, 2018-2021;
- National Natural Science Foundation of China, General Project, The generation of climatic design condition of building HVAC based on probability prediction, 2020-2024;
- National key research project, Co-simulation of distributed integrated energy system , 2017-2021;
- National key research project, Building full performance simulation platform core tech development, 2017-2020;
- Commissioning of HVAC system in Boeing Plant in Zhoushan, 2018-2019
- The generation of operation strategy of Heating sub-station based on data mining, 2018-2019
- Xiong’an city intelligent energy management system - integrated energy system dispatch and operation strategy algorithm development, 2019.10-2020.
1. XY. Lin, Z. Tian, An energy performance assessment method for district heating substations based on energy disaggregation[J], Energy and Buildings, 2022
2. Q. Zhang, Z. Tian. A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data[J], Building & Environment, 2022
3. J. Zhu, J. Niu, Z. Tian, Rapid quantification of demand response potential of building HAVC system via data-driven model[J], Applied Energy, 2022.
4. XY. Hou, Z. Tian, Research on design day generation method for air-conditioning system design considering the coincidence of hourly variation coefficient[J], Energy and Buildings, 2022
5. X. Wu, JD. Niu, Z. Tian, Method of constructing stochastic near-extreme daily weather data for efficient calculation of probabilistic load in air-conditioning system design[J], Building and Environment, 2022
6. SH. Cheng, Z. Tian, Bottom-Up Model of Random Daily Electrical Load Curve for Office Building[J], 2021
7. Y.K. Lu, Z. Tian, A general transfer learning-based framework for thermal load prediction in regional energy system[J], Energy, 2021
10. B. Lan, Z. Tian, X. Wu, A simplified method of generating sequential meteorological parameters for uncertainty-based energy system design[J], Energy and Buildings, 2021
11. L. Wen, Z. Tian, Comparison and selection of operation optimization mode of multi-energy and multi-level district heating system: Case study of a district heating system in Xiong’an[J], Journal of Cleaner Production, 2020
12. YK. Lu, Z. Tian, Multi-step-ahead prediction of thermal load in regional energy system using deep learning method[J], Energy and Buildings, 2020
13. Lan B, Tian Z, Niu J, et al. Applicability analysis of solar heating system in China based on a reliability-based optimization method for auxiliary heater capacity[J]. Sustainable Cities and Society, 2020.
14. J. Niu, Z, Tian, Implementation of a price-driven demand response in a distributed energy system with multi-energy flexibility measures[J]. Energy Conversion and Management, 2020,
15. Lan B, Tian Z, Niu J, et al. Improving the design method of a solar heating system considering weather uncertainty and system reliability[J]. Energy and Buildings, 2020
16. Tian Z, Fu F, Niu J, et al. Optimization and extraction of an operation strategy for the distributed energy system of a research station in Antarctica[J]. Journal of Cleaner Production, 2020
17. Niu J, Tian Z, Yue L. Robust optimal design of building cooling sources considering the uncertainty and cross-correlation of demand and source[J]. Applied Energy, 2020
18. Zhang Q, Tian Z, Ma Z, et al. Development of the heating load prediction model for the residential building of district heating based on model calibration[J]. Energy, 2020
19. Wu X, Tian Z, Wang Y, et al. Method for determining climatic design conditions based on the indoor thermal environment risk level[J]. Energy and Built Environment, 2020.
20. YK. Lu, Z. Tian, GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system[J], Energy and Buildings, 2019
21. Jide Niu, Zhe Tian, Yakai Lu, Hongfang Zhao, Bo Lan, A robust optimization model for designing the building cooling source under cooling load uncertainty[J], Applied Energy, ,2019
22. Jide Niu, Zhe Tian, Yakai Lu, Hongfang Zhao, Flexible dispatch of a building energy system using building thermal storage and battery energy storage[J], Applied Energy, 2019
23. Qiang Zhang, Zhe Tian, Yan Ding, Yakai Lu, Jide Niu, Development and evaluation of cooling load prediction models for a factory workshop[J], Journal of Cleaner Production, 2019
24. Xinyi Lin, Zhe Tian, Yakai Lu, Hejia Zhang, Jide Niu, Short-term forecast model of cooling load using load component disaggregation[J], Applied Thermal Engineering, Volume 157, 5 July 2019
25. LI Yang, TIAN Zhe, QIN Kaiming, NIU Jide, ZHAO Hongfang, Impacts of water storage on robust optimal design of cooling system considering uncertainty[J], Energy Procedia, Volume 159, February 2019, Pages 430-435
26. Wenxuan Han, Zhe Tian, Yuanyuan Wang, Hejia Zhang, Jide Niu, Evaluation on determination method of current climate design conditions in China based on indoor thermal environment risk level[J], Energy, Volume 161, October 2018, Pages 610-617
27. Wancheng Li, Zhe Tian, Yakai Lu, Fawei Fu, Stepwise calibration for residential building thermal performance model using hourly heat consumption data[J], Energy and Buildings, Volume 181, 15 December 2018, Pages 10-25
28. Lu YK, Tian Z. Peng P. Niu JD, Li WC, Zhang HJ, GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system[J], ENERGY AND BUILDINGS, Vol.190, P 49-60, MAY 1 2019
29. Zhe Tian, Jide Niu, Yakai Lu. The improvement of a simulation model for a distributed CCHP system and its influence on optimal operation cost and strategy[J], Applied Energy, Vol165, 1 2016, 430-444
30. Guo Qiang, Tian Zhe, Ding Yan, Zhu Neng. An improved office building cooling load prediction model based on multivariable linear regression[J], Energy and Buildings, Vol107, 15 Nov 2015: 445-455