Ms. Yi Wu | Building Energy Simulation | Best Researcher Award

PhD candidate, Tsinghua University, China

Yi Wu is a dedicated Ph.D. candidate in the Department of Building Science at the School of Architecture, Tsinghua University. With a dual background in engineering and economics, Yi bridges technical acumen and strategic insight in sustainable building practices. His academic focus lies in building thermal resilience, occupant behavior simulation, and big data analytics in HVAC systems. He has contributed as a primary and co-author to several high-impact journals, addressing real-world energy challenges using advanced simulations and data mining techniques. Yi is also recognized for developing national-scale building models and co-simulation algorithms for indoor air quality and energy. As a reviewer for prestigious journals like Building Simulation and Energy and Buildings, he is deeply involved in the academic community. His solid coding and analytical skills, coupled with a TOEFL score of 112, position him as a rising expert in the domain of energy-efficient building technologies and smart city applications.

Professional Profile

• Scopus
• Orcid

🎓 Education

Yi Wu began his academic journey at Tsinghua University, earning a Bachelor’s degree in Building Science (2017–2021), where he completed a thesis on extracting AC behavior patterns from large-scale VRF operation data and achieved a GPA of 3.70/4.0. Simultaneously, he pursued a second Bachelor’s degree in Economics and Management (2018–2021), with a thesis focused on the marketing success of blind box companies, scoring a GPA of 3.84/4.0. In 2021, he continued at Tsinghua as a Ph.D. candidate in Building Science, achieving a GPA of 3.88/4.0 to date. His doctoral research emphasizes building thermal resilience, big data mining in HVAC, and simulation of occupant behavior. Yi’s interdisciplinary educational background allows him to approach architectural challenges with both technical precision and managerial insight. This rare combination enhances his effectiveness in multidisciplinary research on energy efficiency, simulation, and behavioral modeling in smart buildings.

🏅 Awards and Honors

Yi Wu has received several prestigious accolades during his academic journey. He was awarded the National Scholarship, one of the highest academic honors in China, for his exceptional performance and contributions. Additionally, he was recognized as a Beijing Outstanding Graduate and an Excellent Graduate of Tsinghua University at the Bachelor level, signifying academic excellence, leadership, and societal contributions. These honors reflect not only his high GPA but also his active engagement in impactful research and academic service. Yi has also served as a reviewer for internationally reputed SCI journals such as Building Simulation, Energy and Buildings, and Advanced Engineering Informatics, showcasing his technical maturity and peer recognition in the research community. His honors validate his role as a young innovator in sustainable architecture and energy-efficient building systems, making him a deserving candidate for international recognition in the field of building energy simulation and data-driven HVAC optimization.

🔬 Research Focus 

Yi Wu’s research is rooted in building energy simulation, thermal resilience, and HVAC data analytics. He specializes in large-scale data mining from Variable Refrigerant Flow (VRF) systems, developing predictive occupant behavior models, and enhancing simulation accuracy through machine learning. His work aims to optimize energy performance in urban buildings by simulating real occupant actions and leveraging big data to inform policy and design. Yi also explores co-simulation of indoor air quality (IAQ) and energy performance through C++-based algorithm development. Another area of focus includes generating Typical Meteorological Year (TMY) data and establishing national-scale prototype building models for carbon emission assessments. His interdisciplinary approach blends simulation tools (DeST, EnergyPlus), programming (Python, TensorFlow), and empirical datasets (30+ TB VRF data) to support green building development at the city and national levels. Yi’s research ultimately contributes to the digitalization and decarbonization of the built environment.

📚 Publication Top Notes

1. Weather Data Mining
   Wu, Y., An, J., Gui, C., Xiao, C., & Yan, D. (2023). A global typical meteorological year (TMY) database on ERA5 dataset. Building Simulation, 16, 1013–1026. [IF: 5.5]
   Summary: This study constructs a global TMY database based on ERA5 reanalysis data. It supports climate-adaptive building simulation across diverse locations by improving access to standardized weather input data.

2. VRF System Performance
   Liu, H., Wu, Y., Yan, D., Hu, S., & Qian, M. (2022). Investigation of VRF system cooling operation and performance in residential buildings based on large-scale dataset. Journal of Building Engineering, 1052-1019.
   Summary: The authors evaluate operational patterns and efficiency of residential VRF systems using a vast dataset, revealing real-world behaviors that challenge existing design standards.

3. Prototype Building Models
   An, J., Wu, Y., Gui, C., & Yan, D. (2023). Chinese prototype building models for simulating the energy performance of the nationwide building stock. Building Simulation, 16(8), 1559–1582. [IF: 5.5]
   Summary: This paper introduces Chinese prototype models to simulate the nation’s building stock energy consumption, providing a foundation for urban-scale energy policy development.

4. Occupant AC Behavior Modeling
   Wu, Y., Zhou, X., Qian, M., Jin, Y., Sun, H., & Yan, D. (2023). Novel approach to typical air-conditioning behavior pattern extraction based on large-scale VRF system online monitoring data. Journal of Building Engineering, 106243. [IF: 6.4]
   Summary: Yi Wu presents a novel data-driven method to extract typical AC usage patterns, significantly enhancing occupant behavior modeling in HVAC simulation.

5. OB Level-of-Detail Framework
   Wu, Y., An, J., Qian, M., & Yan, D. (2023). Application-driven level-of-detail modeling framework for occupant air-conditioning behavior in district cooling. Journal of Building Engineering, 70. [IF: 6.4]
   Summary: Proposes a flexible modeling framework adjusting detail levels of occupant behavior to improve simulation accuracy under district cooling scenarios.

6. Renewable Energy Systems Integration
   Huang, P., Zhang, X., Copertaro, B., Saini, P. K., Yan, D., Wu, Y., & Chen, X. (2020). A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS). Sustainability, 12(17), 7035. [IF: 4.0]
   Summary: Reviews integrated modeling strategies for urban solar systems spanning buildings, electric vehicles, and storage, promoting energy synergy and carbon reduction.

Conclusion

Yi Wu is a highly promising researcher at the intersection of building science, data mining, and sustainability. His interdisciplinary training, impactful publications, and technical versatility make him a strong and deserving candidate for the Best Researcher Award 🏅. With continued growth in leadership and outreach, he is poised to make lasting contributions to the field.