| Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning Z Zhang, A Chong, Y Pan, C Zhang, KP Lam Energy and Buildings 199, 472-490, 2019 | 302 | 2019 |
| Green and cool roofs’ urban heat island mitigation potential in tropical climate J Yang, A Pyrgou, A Chong, M Santamouris, D Kolokotsa, SE Lee Solar Energy 173, 597-609, 2018 | 198 | 2018 |
| Generative adversarial network for fault detection diagnosis of chillers K Yan, A Chong, Y Mo Building and Environment 172, 106698, 2020 | 176 | 2020 |
| Calibrating building energy simulation models: A review of the basics to guide future work A Chong, Y Gu, H Jia Energy and Buildings 253, 111533, 2021 | 165 | 2021 |
| Guidelines for the Bayesian calibration of building energy models A Chong, K Menberg Energy and Buildings 174, 527-547, 2018 | 142 | 2018 |
| Bayesian calibration of building energy models with large datasets A Chong, KP Lam, M Pozzi, J Yang Energy and Buildings 154, 343-355, 2017 | 134 | 2017 |
| A deep reinforcement learning approach to using whole building energy model for hvac optimal control Z Zhang, A Chong, Y Pan, C Zhang, S Lu, KP Lam 2018 Building Performance Analysis Conference and SimBuild 3, 22-23, 2018 | 132 | 2018 |
| Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective S Zhan, A Chong Renewable and Sustainable Energy Reviews 142, 110835, 2021 | 115 | 2021 |
| Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning D Zhuang, VJL Gan, ZD Tekler, A Chong, S Tian, X Shi Applied Energy 338, 120936, 2023 | 105 | 2023 |
| Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking S Zhan, Z Liu, A Chong, D Yan Applied energy 269, 114920, 2020 | 97 | 2020 |
| Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy ZD Tekler, A Chong Building and Environment 226, 109689, 2022 | 93 | 2022 |
| Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial–temporal proximity data from Build2Vec MM Abdelrahman, A Chong, C Miller Building and Environment 207, 108532, 2022 | 85 | 2022 |
| Building occupancy and energy consumption: Case studies across building types S Zhan, A Chong Energy and Built Environment 2 (2), 167-174, 2021 | 80 | 2021 |
| Performance evaluation of misting fans in hot and humid climate NH Wong, AZM Chong Building and Environment 45 (12), 2666-2678, 2010 | 79 | 2010 |
| Building occupancy forecasting: A systematical and critical review Y Jin, D Yan, A Chong, B Dong, J An Energy and buildings 251, 111345, 2021 | 78 | 2021 |
| Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review B Dong, Y Liu, H Fontenot, M Ouf, M Osman, A Chong, S Qin, F Salim, ... Applied Energy 293, 116856, 2021 | 73 | 2021 |
| Improving evolutionary algorithm performance for integer type multi-objective building system design optimization W Xu, A Chong, OT Karaguzel, KP Lam Energy and Buildings 127, 714-729, 2016 | 73 | 2016 |
| A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings Y Lei, S Zhan, E Ono, Y Peng, Z Zhang, T Hasama, A Chong Applied Energy 324, 119742, 2022 | 72 | 2022 |
| Occupancy data at different spatial resolutions: Building energy performance and model calibration A Chong, G Augenbroe, D Yan Applied Energy 286, 116492, 2021 | 68 | 2021 |
| Continuous-time Bayesian calibration of energy models using BIM and energy data A Chong, W Xu, S Chao, NT Ngo Energy and Buildings 194, 177-190, 2019 | 68 | 2019 |
Sicheng ZhanPostdoc, MITПотвърден имейл адрес: u.nus.edu
