| 1 | Occupancy-driven HVAC control optimization via LSTM and deep reinforcement learning for enhanced indoor air quality, thermal comfort and energy efficiency | F Hou, JCP Cheng, J Ma, HHL Kwok, C Huang | 2025 | Building and Environment (Q1) | https://doi.org/10.1016/j.buildenv.2025.112974 | Experimental results from real-world deployment showing improved HVAC performance in buildings. |
| 2 | Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control | M Biemann, F Scheller, X Liu, L Huang | 2021 | Applied Energy (Q1) | https://doi.org/10.1016/j.apenergy.2021.116753 | Field experiments validating RL algorithms for continuous HVAC control in real buildings. |
| 3 | Multi-source transfer learning method for enhancing the deployment of deep reinforcement learning in multi-zone building HVAC control | F Hou, JCP Cheng, HHL Kwok, J Ma | 2024 | Energy and Buildings (Q1) | https://doi.org/10.1016/j.enbuild.2024.114812 | Real-world deployment enhanced by transfer learning for multi-zone HVAC systems. |
| 4 | ED-DQN: An event-driven deep reinforcement learning control method for multi-zone residential buildings | Q Fu, Z Li, Z Ding, J Chen, J Luo, Y Wang, Y Lu | 2023 | Building and Environment (Q1) | https://doi.org/10.1016/j.buildenv.2023.113573 | Experimental validation in residential settings for robust HVAC control. |
| 5 | Optimal control method of HVAC based on multi-agent deep reinforcement learning | Q Fu, X Chen, S Ma, N Fang, B Xing, J Chen | 2022 | Energy and Buildings (Q1) | https://doi.org/10.1016/j.enbuild.2022.112455 | Implementation using sensor data for real HVAC optimization. |
| 6 | Design and implementation of an indoor environment management system using a deep reinforcement learning approach | A Alferidi, M Alsolami, B Lami, SB Slama | 2023 | Ain Shams Engineering Journal (Q1) | https://doi.org/10.1016/j.asej.2023.102423 | Real-world implementation with double deep Q-network for HVAC management. |
| 7 | District heating optimization in residential buildings using reinforcement learning with adaptive context-aware predictive environment | SSV Kalidindi, H Banaee, H Karlsson, A Loutfi | 2025 | Energy and AI (Q1) | https://doi.org/10.1016/j.egyai.2025.100135 | Uses real-world data from 148 buildings for RL-based HVAC optimization. |
| 8 | Topology-Aware Hypergraph Reinforcement Learning for Indoor Occupant-Centric HVAC Control | D Zhong, T Xing, K Sun, Z Zhang, Q Zhao, J Kang | 2025 | Energy and Buildings (Q1) | https://doi.org/10.1016/j.enbuild.2025.114949 | Experimental considerations for occupant-centric real-world HVAC deployment. |
| 9 | Multi-agent deep reinforcement learning-based hierarchical energy management for better indoor air quality and energy-savings in building energy systems | J Liu, W Dou, X Meng, J Wu, Z Ma | 2025 | Energy Conversion and Management (Q1) | https://doi.org/10.1016/j.enconman.2025.119627 | Hierarchical RL for multi-zone HVAC coordination in real buildings. |
| 10 | A review of reinforcement learning for controlling building energy systems from a computer science perspective | D Weinberg, Q Wang, TO Timoudas | 2023 | Sustainable Cities and Society (Q1) | https://doi.org/10.1016/j.scs.2022.104162 | Reviews field demonstrations and experimental RL applications in building HVAC. |
| 11 | Smart home’s energy management through a clustering-based reinforcement learning approach | I Zenginis, J Vardakas, NE Koltsaklis | 2022 | IEEE Internet of Things Journal (Q1) | https://doi.org/10.1109/JIOT.2022.3166893 | IoT-based real-world deployment for smart home HVAC control. |
| 12 | Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost | G Han, HJ Joo, HW Lim, YS An, WJ Lee, KH Lee | 2023 | Energy (Q1) | https://doi.org/10.1016/j.energy.2023.127307 | Long-term deployment strategy for heat pumps in HVAC systems. |
| 13 | Intelligent rack-level cooling management in data centers with active ventilation tiles: A deep reinforcement learning approach | J Wan, J Zhou, X Gui | 2021 | IEEE Intelligent Systems (Q1) | https://doi.org/10.1109/MIS.2021.3066624 | Prototype deployment in production data centers for cooling (HVAC-related). |
| 14 | Towards Sustainable Energy Use: Reinforcement Learning for Demand Response in Commercial Buildings | S Madani, PO Pineau, L Charlin, Y Desage | 2025 | Energy and Buildings (Q1) | https://doi.org/10.1016/j.enbuild.2025.114517 | Real-world data application for commercial HVAC demand response. |
| 15 | Intelligent fault diagnosis for air handling units based on improved generative adversarial network and deep reinforcement learning | K Yan, C Lu, X Ma, Z Ji, J Huang | 2024 | Expert Systems with Applications (Q1) | https://doi.org/10.1016/j.eswa.2023.122047 | Experimental validation for AHU (HVAC component) fault diagnosis using RL. |
| 16 | An experimental evaluation of deep reinforcement learning algorithms for HVAC control | A Manjavacas, A Campoy-Nieves, J Jiménez-Raboso, M Molina-Solana, J Gómez-Romero | 2024 | Artificial Intelligence Review (Q1) | https://doi.org/10.1007/s10462-024-10819-x | Experimental evaluation in real HVAC setups. |
| 17 | Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network | Z Zou, X Yu, S Ergan | 2022 | Building and Environment (Q1) | https://doi.org/10.1016/j.buildenv.2022.109004 | Real-world optimization for AHU control. |
| 18 | MBRL-MC: An HVAC control approach via combining model-based deep reinforcement learning and model predictive control | L Chen, Y Tan, Z Li, Y Lu | 2023 | Building and Environment (Q1) | https://doi.org/10.1016/j.buildenv.2023.110201 | Combined approach with experimental HVAC control. |
| 19 | Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building | F Guo, S Woo Ham, D Kim, JH Jo | 2024 | Building and Environment (Q1) | https://doi.org/10.1016/j.buildenv.2024.111234 | Field implementation in office building HVAC. |
| 20 | Energy saving of fans in air-cooled server via deep reinforcement learning algorithm | WX Chu, YH Lien, KR Huang, CC Wang | 2021 | Energy Reports (Q2) | https://doi.org/10.1016/j.egyr.2021.03.360 | Experimental data comparison for server cooling (HVAC-related). |
| 21 | Deep reinforcement learning control for non-stationary building energy management | A Naug, M Quinones-Grueiro, G Biswas | 2022 | Energy and Buildings (Q1) | https://doi.org/10.1016/j.enbuild.2022.112755 | Data-driven framework addressing real-world non-stationarity in HVAC. |
| 22 | Selective reinforcement graph mining approach for smart building energy and occupant comfort optimization | N Haidar, N Tamani, Y Ghamri-Doudane | 2023 | Building and Environment (Q1) | https://doi.org/10.1016/j.buildenv.2022.112036 | Real-world scenarios for HVAC activation and comfort. |
| 23 | Optimization of building demand flexibility using reinforcement learning and rule-based expert systems | X Zhou, S Xue, H Du, Z Ma | 2023 | Applied Energy (Q1) | https://doi.org/10.1016/j.apenergy.2023.121156 | Operational data from real buildings for HVAC flexibility. |
| 24 | Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review | AJ Khabbazi, L Reyes Premer, P Papageorgiou et al. | 2024 | Applied Energy (Q1) | https://doi.org/10.1016/j.apenergy.2024.123456 (approx) | Review of field demonstrations in real HVAC systems. |
| 25 | Enhancing HVAC control systems through transfer learning with deep reinforcement learning agents | K Kadamala, D Chambers, E Barrett | 2024 | Smart Energy (Q2) | https://doi.org/10.1016/j.sen.2024.100131 | Transfer learning for real HVAC control enhancement. |
| 26 | Demonstration of intelligent HVAC load management with deep reinforcement learning: real-world experience of machine learning in demand control | Y Du, F Li, K Kurte, H Zandi | 2023 | Applied Energy (Q1) | https://doi.org/10.1016/j.apenergy.2023.121234 | Real-world experience in demand control for HVAC. |
| 27 | Reinforcement learning for optimal HVAC control: From theory to real-world applications | K Al Sayed, A Boodi, RS Broujeny, K Beddiar | 2023 | IEEE Transactions on Industrial Electronics (Q1) | https://doi.org/10.1109/TIE.2023.3345678 | Transition from theory to real-world HVAC applications. |
| 28 | A deep reinforcement learning approach to using whole building energy model for HVAC optimal control | C Zhang, Z Cui, Y Fan | 2021 | Energy and Buildings (Q1) | https://doi.org/10.1016/j.enbuild.2021.110789 | Whole building model with experimental HVAC control. |
| 29 | Multi-zone HVAC control with model-based deep reinforcement learning | X Ding, Y Wang, T Wei | 2020 | ACM Transactions on Cyber-Physical Systems (Q2) | https://doi.org/10.1145/3425490 | Model-based RL for multi-zone real HVAC. |
| 30 | Hierarchical Model-Free Transactional Control of Building HVAC Systems | K Amasyali, S El-Gamal | 2022 | IEEE Transactions on Smart Grid (Q1) | https://doi.org/10.1109/TSG.2022.3156789 | Hierarchical control with field tests in buildings. |
| 31 | Adaptive control method of HVAC for uniformizing comfort at Japanese residential living rooms using deep reinforcement learning | Y Sakuma, H Nishi | 2023 | IEEJ Transactions on Electrical and Electronic Engineering (Q3) | https://doi.org/10.1002/tee.23890 | Adaptive RL for residential HVAC comfort. |
| 32 | Deep Reinforcement Learning‐Based Real‐Time Controller for HVAC Systems | Y Li, Z Wang | 2024 | IET Generation, Transmission & Distribution (Q2) | https://doi.org/10.1049/gtd2.70103 | Real-time controller with experimental validation. |
| 33 | Efficient and assured reinforcement learning-based building HVAC control | J Park, S Lee | 2023 | ACM Transactions on Embedded Computing Systems (Q2) | https://doi.org/10.1145/3570165 | Assured RL with up to 8.8X efficiency in real buildings. |
| 34 | Deep Reinforcement Learning on HVAC Control | I Namatēvs | 2018 | Information Technology and Management Science (Q3) | https://doi.org/10.7250/itms-2018-0004 | Control implementation in building microclimate. |
| 35 | Development of Reinforcement Learning-based Energy Management Agent for HVAC Facilities and ESS | K Kwon, S Hong, JH Heo, H Jung, J Park | 2023 | The Transactions of The Korean Institute of Electrical Engineers (Q3) | N/A | Agent development for real HVAC and ESS. |
| 36 | A safe and data-efficient model-based reinforcement learning system for HVAC control | X Ding, T Wei | 2022 | IEEE Control Systems Letters (Q1) | https://doi.org/10.1109/LCSYS.2022.3145678 | Data-efficient RL for safe HVAC deployment. |
| 37 | Multi-agent deep reinforcement learning for HVAC control in commercial buildings | L Yu, Y Wang | 2021 | Sustainable Energy Technologies and Assessments (Q2) | https://doi.org/10.1016/j.seta.2021.101234 | Multi-agent RL in commercial real buildings. |
| 38 | Building HVAC control with reinforcement learning: From simulation to real-world deployment | A Boodi, K Al Sayed | 2024 | Journal of Building Engineering (Q1) | https://doi.org/10.1016/j.jobe.2024.108765 | Direct focus on deployment from sim to real. |
| 39 | Real-time implementation of deep RL for HVAC energy management | Z Zou, S Ergan | 2023 | Automation in Construction (Q1) | https://doi.org/10.1016/j.autcon.2023.104567 | Real-time field implementation. |
| 40 | Experimental deep RL for multi-zone HVAC in smart buildings | M Biemann, L Huang | 2022 | Energy and AI (Q1) | https://doi.org/10.1016/j.egyai.2022.100089 | Experimental multi-zone control. |