| Energy Flexibility of Residential Buildings: A Systematic Review of Characterization and Quantification Methods and Applications H Li, Z Wang, T Hong, MA Piette Advances in Applied Energy, 2021 | 423 | 2021 |
| Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives H Li, H Johra, F de Andrade Pereira, T Hong, J Le Dréau, A Maturo, ... Applied Energy 343, 121217, 2023 | 134 | 2023 |
| System-level key performance indicators for building performance evaluation H Li, T Hong, SH Lee, M Sofos Energy and Buildings 209, 109703, 2020 | 120 | 2020 |
| End-Use Load Profiles for the US Building Stock: Methodology and Results of Model Calibration, Validation, and Uncertainty Quantification EJH Wilson, A Parker, A Fontanini, E Present, JL Reyna, R Adhikari, ... | 117 | 2022 |
| Ten questions concerning reinforcement learning for building energy management Z Nagy, G Henze, S Dey, J Arroyo, L Helsen, X Zhang, B Chen, ... Building and Environment 241, 110435, 2023 | 116 | 2023 |
| Predicting City-Scale Daily Electricity Consumption Using Data-Driven Models Z Wang, T Hong, H Li, MA Piette Advances in Applied Energy, 2021 | 110 | 2021 |
| Characterizing Patterns and Variability of Building Electric Load Profiles in Time and Frequency Domains H Li, Z Wang, T Hong, A Parker, M Neukomm Applied Energy, 2021 | 67 | 2021 |
| A semantic ontology for representing and quantifying energy flexibility of buildings H Li, T Hong Advances in Applied Energy 8, 100113, 2022 | 60 | 2022 |
| Occupant-Centric Key Performance Indicators to Inform Building Design and Operations H Li, Z Wang, T Hong Building Performance Simulation, 2021 | 58 | 2021 |
| Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings Z Wang, T Hong, H Li Environmental Research Letters 16 (7), 074003, 2021 | 50 | 2021 |
| Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics G Pinto, R Messina, H Li, T Hong, MS Piscitelli, A Capozzoli Energy and Buildings 276, 112530, 2022 | 48 | 2022 |
| End-use load profiles for the US building stock E Wilson, A Parker, A Fontanini, E Present, J Reyna, R Adhikari, ... DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory …, 2021 | 48 | 2021 |
| Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control W Liang, H Li, S Zhan, A Chong, T Hong Advances in Applied Energy 14, 100167, 2024 | 45 | 2024 |
| CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities K Nweye, K Kaspar, G Buscemi, T Fonseca, G Pinto, D Ghose, ... Journal of Building Performance Simulation 18 (1), 17-38, 2025 | 41 | 2025 |
| AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination Z Wang, B Chen, H Li, T Hong Advances in Applied Energy 4, 100061, 2021 | 41 | 2021 |
| An inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data H Li, T Hong, M Sofos Energy and Buildings 198, 228-242, 2019 | 40 | 2019 |
| Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset H Li, G Pinto, M Piscitelli, A Capozzoli, T Hong Engineering Applications of Artificial Intelligence 130 (107701), 2024 | 35 | 2024 |
| End-use load profiles for the US building stock: Market needs, use cases, and data gaps NM Frick, E Wilson, J Reyna, A Parker, E Present, J Kim, T Hong, H Li, ... | 33 | 2019 |
| On data-driven energy flexibility quantification: A framework and case study H Li, T Hong Energy and Buildings 296, 113381, 2023 | 24 | 2023 |
| Physics-informed machine learning for building performance simulation-A review of a nascent field Z Jiang, X Wang, H Li, T Hong, F You, J Drgoňa, D Vrabie, B Dong Advances in Applied Energy, 100223, 2025 | 23 | 2025 |