| A Framework for Quantifying the Impact of Occupant Behavior on Energy Savings of Energy Conservation Measures K Sun, T Hong Energy and Buildings 146, 383–396, 2017 | 203 | 2017 |
| Building simulation: Ten challenges T Hong, J Langevin, K Sun Building Simulation 11, 871-898, 2018 | 197 | 2018 |
| Commercial Building Energy Saver: An Energy Retrofit Analysis Toolkit T Hong, Y Chen, SH Lee, R Zhang, K Sun, SC Taylor-Lange, MA Piette, ... Applied Energy 159, 298-309, 2015 | 197 | 2015 |
| A Simulation Approach to Estimate Energy Savings Potential of Occupant Behavior Measures K Sun, T Hong Energy and Buildings 136, 43–62, 2017 | 142 | 2017 |
| Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration K Sun, D Yan, T Hong, S Guo Building and Environment 79, 1-12, 2014 | 134 | 2014 |
| A pattern-based automated approach to building energy model calibration K Sun, T Hong, SC Taylor-Lange, MA Piette Applied Energy 165, 214-224, 2016 | 114 | 2016 |
| Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings X Yu, D Yan, K Sun, T Hong, D Zhu Applied Energy 183, 725-736, 2016 | 112 | 2016 |
| A novel stochastic modeling method to simulate cooling loads in residential districts J An, D Yan, T Hong, K Sun Applied Energy 206, 134-149, 2017 | 100 | 2017 |
| Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in … B Tarroja, F Chiang, A AghaKouchak, S Samuelsen, SV Raghavan, ... Applied Energy 225, 522-534, 2018 | 96 | 2018 |
| Development and validation of a new variable refrigerant flow system model in EnergyPlus T Hong, K Sun, R Zhang, R Hinokuma, S Kasahara, Y Yura Energy and Buildings 117, 399-411, 2016 | 89 | 2016 |
| Nexus of thermal resilience and energy efficiency in buildings: A case study of a nursing home K Sun, M Specian, T Hong Building and Environment, 106842, 2020 | 82 | 2020 |
| Advanced Building Control via Deep Reinforcement Learning R Jia, M Jin, K Sun, T Hong, C Spanos Energy Procedia 158, 6158-6163, 2019 | 82 | 2019 |
| Italian prototype building models for urban scale building performance simulation L Carnieletto, M Ferrando, L Teso, K Sun, W Zhang, F Causone, ... Building and Environment 192, 107590, 2021 | 81 | 2021 |
| Coupling CFD and building energy modelling to optimize the operation of a large open office space for occupant comfort X Shan, N Luo, K Sun, T Hong, YK Lee, WZ Lu Sustainable Cities and Society 60, 102257, 2020 | 67 | 2020 |
| Quantifying the benefits of a building retrofit using an integrated system approach: A case study C Regnier, K Sun, T Hong, MA Piette Energy and Buildings 159, 332-345, 2018 | 56 | 2018 |
| Passive cooling designs to improve heat resilience of homes in underserved and vulnerable communities K Sun, W Zhang, Z Zeng, R Levinson, M Wei, T Hong Energy and Buildings 252, 111383, 2021 | 47 | 2021 |
| A novel Variable Refrigerant Flow (VRF) heat recovery system model: Development and validation R Zhang, K Sun, T Hong, Y Yura, R Hinokuma Energy and Buildings 168, 399-412, 2018 | 45 | 2018 |
| Urban microclimate and its impact on building performance: A case study of San Francisco T Hong, Y Xu, K Sun, W Zhang, X Luo, B Hooper Urban Climate 38, 100871, 2021 | 44 | 2021 |
| Prototype energy models for data centers K Sun, N Luo, X Luo, T Hong Energy and Buildings 231, 110603, 2021 | 34 | 2021 |
| Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California N Luo, W Weng, X Xu, T Hong, M Fu, K Sun Science of The Total Environment 686, 1251-1261, 2019 | 34 | 2019 |
Tianzhen HongSenior Scientist, PhD, FIBPSA, FASHRAE, Lawrence Berkeley National LabVerified email at lbl.gov
