1. From theory to practice: A critical review of model predictive control field implementations in the built environment. (2025). Zhang, Kun ; Saloux, Etienne ; Candanedo, Jos A ; Vallianos, Charalampos ; Morovat, Navid.
   In: Applied Energy.
   RePEc:eee:appene:v:393:y:2025:i:c:s0306261925008219.

   Full description at Econpapers || Download paper

2. A review of physics-informed machine learning for building energy modeling. (2025). Jiang, Gang ; Ma, Zhihao ; Chen, Jianli ; Hu, Yuqing.
   In: Applied Energy.
   RePEc:eee:appene:v:381:y:2025:i:c:s0306261924025534.

   Full description at Econpapers || Download paper

1. Anderson, C.W. ; Hittle, D.C. ; Katz, A.D. ; Kretchmar, R.M. Synthesis of reinforcement learning, neural networks and PI control applied to a simulated heating coil. 1997 Artific Intellig Eng. 11 421-429
   Paper not yet in RePEc: Add citation now
   

2. Arroyo, J. ; Manna, C. ; Spiessens, F. ; Helsen, L. Reinforced model predictive control (RL-MPC) for building energy management. 2022 Appl Energy. 309 -

3. Arroyo, J. ; Spiessens, F. ; Helsen, L. Comparison of optimal control techniques for building energy management. 2022 Front Built Environ. 8 -
   Paper not yet in RePEc: Add citation now
   
4. ASHRAE, Guideline 36, high-performance sequences of operation for HVAC systems. 2024 :
   Paper not yet in RePEc: Add citation now
   
5. Barrett, E. ; Linder, S. Autonomous HVAC control, a reinforcement learning approach. 2015 En : Lecture Notes in Computer Science. :
   Paper not yet in RePEc: Add citation now
   

6. Biemann, M. ; Scheller, F. ; Liu, X. ; Huang, L. Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control. 2021 Appl Energy. 298 -

7. Blum, D. ; Arroyo, J. ; Huang, S. ; Drgoňa, J. ; Jorissen, F. ; Walnum, H.T. Building optimization testing framework (BOPTEST) for simulation-based benchmarking of control strategies in buildings. 2021 J Build Perform Simulat. 14 586-610
   Paper not yet in RePEc: Add citation now
   

8. Blum, D. ; Wang, Z. ; Weyandt, C. ; Kim, D. ; Wetter, M. ; Hong, T. Field demonstration and implementation analysis of model predictive control in an office HVAC system. 2022 Appl Energy. 318 -

9. Chen, B. ; Cai, Z. ; Bergés, M. GNU-RL: a practical and scalable reinforcement learning solution for building HVAC control using a differentiable MPC policy. 2020 Front Built Environ. 6 -
   Paper not yet in RePEc: Add citation now
   
10. Dalamagkidis, K. ; Κολοκότσα, Δ. ; Kalaitzakis, K. ; Stavrakakis, G. Reinforcement learning for energy conservation and comfort in buildings. 2007 Build Environ. 42 2686-2698
    Paper not yet in RePEc: Add citation now
    
11. De Coninck, R. ; Helsen, L. Practical implementation and evaluation of model predictive control for an office building in Brussels. 2016 Energ Buildings. 111 290-298
    Paper not yet in RePEc: Add citation now
    

12. Di Natale, L. ; Svetozarevic, B. ; Heer, P. ; Jones, C.N. Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models. 2023 Appl Energy. 340 -

13. Dong, B. ; Lam, K.P. A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. 2013 Build Simulat. 7 89-106
    Paper not yet in RePEc: Add citation now
    
14. Drgoňa, J. ; Arroyo, J. ; Figueroa, I.C. ; Blum, D. ; Arendt, K. ; Kim, D. All you need to know about model predictive control for buildings. 2020 Ann Rev Control. 50 190-232
    Paper not yet in RePEc: Add citation now
    
15. Drgoňa, J. ; Kiš, K. ; Tuor, A. ; Vrabie, D. ; Klaučo, M. Differentiable predictive control: deep learning alternative to explicit model predictive control for unknown nonlinear systems. 2022 J Process Control. 116 80-92
    Paper not yet in RePEc: Add citation now
    
16. Drgoňa, J. ; Picard, D. ; Helsen, L. Cloud-based implementation of white-box model predictive control for a GEOTABS office building: a field test demonstration. 2020 J Process Control. 88 63-77
    Paper not yet in RePEc: Add citation now
    

17. Drgoňa, J. ; Picard, D. ; Kvasnica, M. ; Helsen, L. Approximate model predictive building control via machine learning. 2018 Appl Energy. 218 199-216

18. Drgoňa, J. ; Tuor, A. ; Chandan, V. ; Vrabie, D. Physics-constrained deep learning of multi-zone building thermal dynamics. 2021 Energ Buildings. 243 -
    Paper not yet in RePEc: Add citation now
    
19. Drgoňa, J. ; Tuor, A. ; Skomski, E. ; Vasisht, S. ; Vrabie, D. Deep learning explicit differentiable predictive control laws for buildings. 2021 IFAC-PapersOnLine. 54 14-19
    Paper not yet in RePEc: Add citation now
    
20. Du, D. ; Fei, M. A two-layer networked learning control system using actor–critic neural network. 2008 Appl Math Comput. 205 26-36
    Paper not yet in RePEc: Add citation now
    
21. Dulac-Arnold, G. ; Levine, N. ; Mankowitz, D.J. ; Li, J. ; Păduraru, C. ; Gowal, S. Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. 2021 Machine Learn. 110 2419-2468
    Paper not yet in RePEc: Add citation now
    
22. Han, X. ; Malkawi, A. Model-free reinforcement learning-based control for radiant floor heating systems. 2023 En : Environmental Science and Engineering. :
    Paper not yet in RePEc: Add citation now
    
23. Hao, H. ; Chen, L. ; Hu, E. A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study. 2015 Build Environ. 89 203-216
    Paper not yet in RePEc: Add citation now
    
24. Hilliard, T. ; Swan, L.G. ; Qin, Z. Experimental implementation of whole building MPC with zone based thermal comfort adjustments. 2017 Build Environ. 125 326-338
    Paper not yet in RePEc: Add citation now
    

25. Jiang, Z. ; Deng, Z. ; Wang, X. ; Dong, B. PANDEMIC: occupancy driven predictive ventilation control to minimize energy consumption and infection risk. 2023 Appl Energy. 334 -

26. Joe, J. ; Karava, P. A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings. 2019 Appl Energy. 245 65-77

27. Karniadakis, G.E. ; Kevrekidis, I.G. ; Lu, L. Physics-informed machine learning. 2021 Nat Rev Phys. 3 422-440
    Paper not yet in RePEc: Add citation now
    

28. Kong, M. ; Dong, B. ; Zhang, R. ; O’Neill, Z. HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study. 2022 Appl Energy. 306 -

29. Kvasnica, M. Implicit vs explicit MPC — Similarities, differences, and a path towards a unified method. 2016 En : 2016 European Control Conference (ECC), Aalborg, Denmark. :
    Paper not yet in RePEc: Add citation now
    
30. Langevin, J. ; Harris, C. ; Satre-Meloy, A. ; Putra, H.C. ; Speake, A. ; Present, E. US building energy efficiency and flexibility as an electric grid resource. 2021 Joule. 5 2102-2128
    Paper not yet in RePEc: Add citation now
    

31. Lei, Y. ; Song, Z. ; Ono, E. ; Peng, Y. ; Zhang, Z. ; Hasama, T. A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings. 2022 Appl Energy. 324 -

32. Li, P. ; Vrabie, D. ; Li, D. ; Bengea, S. ; Mijanovic, S. ; O’Neill, Z. Simulation and experimental demonstration of model predictive control in a building HVAC system. 2015 Sci Technol Built Environ. 21 721-732
    Paper not yet in RePEc: Add citation now
    
33. Liang, W. ; Li, H. ; Zhan, S. ; Chong, A. ; Hong, T. Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control. 2024 Adv Appl Energy. 100167 -
    Paper not yet in RePEc: Add citation now
    
34. Lindelöf, D. ; Afshari, H. ; Alisafaee, M. ; Biswas, J. ; Caban, M. ; Mocellin, X. Field tests of an adaptive, model-predictive heating controller for residential buildings. 2015 Energ Buildings. 99 292-302
    Paper not yet in RePEc: Add citation now
    
35. Mariano-Hernández, D. ; Hernández-Callejo, L. ; Zorita-Lamadrid, Á.L. ; Duque-Pérez, Ó. ; García, F.S. A review of strategies for building energy management system: model predictive control, demand side management, optimization, and fault detect & diagnosis. 2021 J Build Eng. 33 -
    Paper not yet in RePEc: Add citation now
    
36. Mehrotra, K. ; Mohan, C.K. ; Ranka, S. Elements of artificial neural networks. 1997 MIT Press:
    Paper not yet in RePEc: Add citation now
    
37. Park, J.Y. ; Dougherty, T. ; Fritz, H. ; Nagy, Z. LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning. 2019 Build Environ. 147 397-414
    Paper not yet in RePEc: Add citation now
    

38. Ruelens, F. ; Iacovella, S. ; Claessens, B. ; Belmans, R. Learning agent for a heat-pump thermostat with a set-Back strategy using model-free reinforcement learning. 2015 Energies. 8 8300-8318

39. Song, Y. ; Romero, A. ; Müller, M. ; Koltun, V. ; Scaramuzza, D. Reaching the limit in autonomous racing: optimal control versus reinforcement learning. 2023 Sci Robot. 8 -
    Paper not yet in RePEc: Add citation now
    
40. Song, Z. ; Lei, Y. ; Chong, A. Comparing model predictive control and reinforcement learning for the optimal operation of building-PV-battery systems. 2023 E3S Web Conf. 396 04018-
    Paper not yet in RePEc: Add citation now
    
41. Stoffel, P. ; Maier, L. ; Kümpel, A. ; Schreiber, T. ; Müller, D. Evaluation of advanced control strategies for building energy systems. 2023 Energ Buildings. 280 -
    Paper not yet in RePEc: Add citation now
    
42. Sutton, R.S. ; Barto, A.G. Reinforcement learning. 2018 En : An Introduction. MIT Press:
    Paper not yet in RePEc: Add citation now
    

43. Taniguchi, I. ; Watari, D. ; Ozawa, Y. ; Taniguchi, I. ; Suzuki, T. ; Shimoda, Y. Data-driven online energy management framework for HVAC systems: An experimental study. 2023 Appl Energy. 352 -

44. Wang, D. ; Zheng, W. ; Wang, Z. ; Wang, Y. ; Pang, X. ; Wang, W. Comparison of reinforcement learning and model predictive control for building energy system optimization. 2023 Appl Therm Eng. 228 -
    Paper not yet in RePEc: Add citation now
    
45. Wang, X. ; Dong, B. Development of a data-driven predictive control based on a novel physics-informed neural network. 2023 En : Building Simulation Conference Proceedings. :
    Paper not yet in RePEc: Add citation now
    
46. Wang, X. ; Dong, B. Physics-informed hierarchical data-driven predictive control for building HVAC systems to achieve energy and health nexus. 2023 Energ Buildings. 291 -
    Paper not yet in RePEc: Add citation now
    
47. Wang, X. ; Kang, X. ; An, J. ; Chen, H. ; Yan, D. Reinforcement learning approach for optimal control of ice-based thermal energy storage (TES) systems in commercial buildings. 2023 Energ Buildings. 301 -
    Paper not yet in RePEc: Add citation now
    

48. Wang, Z. ; Hong, T. Reinforcement learning for building controls: the opportunities and challenges. 2020 Appl Energy. 269 -

49. Xiao, T. ; You, F. Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization. 2023 Appl Energy. 342 -

50. Yang, L. ; Nagy, Z. ; Goffin, P. ; Schlueter, A. Reinforcement learning for optimal control of low exergy buildings. 2015 Appl Energy. 156 577-586

51. Yang, S. ; Wan, M.P. ; Chen, W. ; Ng, B.F. ; Dubey, S. Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. 2020 Appl Energy. 271 -

52. Yang, S. ; Wan, M.P. ; Ng, B.F. ; Dubey, S. ; Henze, G.P. ; Chen, W. Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system. 2020 Appl Energy. 257 -

53. Yang, Z. ; Gaidhane, A.D. ; Drgoňa, J. ; Chandan, V. ; Halappanavar, M. ; Liu, F. Physics-constrained graph modeling for building thermal dynamics. 2024 Energy AI. 16 -
    Paper not yet in RePEc: Add citation now
    
54. Yu, Z. ; Dexter, A. Online tuning of a supervisory fuzzy controller for low-energy building system using reinforcement learning. 2010 Control Eng Pract. 18 532-539
    Paper not yet in RePEc: Add citation now
    
55. Zhang, Z. ; Chong, A. ; Pan, Y. ; Zhang, C. ; Lam, K.P. Whole building energy model for HVAC optimal control: a practical framework based on deep reinforcement learning. 2019 Energ Buildings. 199 472-490
    Paper not yet in RePEc: Add citation now
    
56. Zou, Z. ; Yu, X. ; Ergan, S. Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network. 2020 Build Environ. 168 -
    Paper not yet in RePEc: Add citation now
    

1. An End-to-End Relearning Framework for Building Energy Optimization. (2025). Biswas, Gautam ; Quinones-Grueiro, Marcos ; Naug, Avisek.
   In: Energies.
   RePEc:gam:jeners:v:18:y:2025:i:6:p:1408-:d:1610973.

   Full description at Econpapers || Download paper

2. Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control. (2025). Li, Lanxin ; Pei, Wenhui ; Zhang, QI.
   In: Energies.
   RePEc:gam:jeners:v:18:y:2025:i:10:p:2588-:d:1657616.

   Full description at Econpapers || Download paper

3. Research on multi-objective optimization control of diesel engine combustion process based on model predictive control-guided reinforcement learning method. (2025). Wang, Zhe ; Ju, Peng ; Shi, Lei ; Deng, Kangyao ; Chen, Ziqiang.
   In: Energy.
   RePEc:eee:energy:v:325:y:2025:i:c:s0360544225018158.

   Full description at Econpapers || Download paper

4. A review on electric vehicle charging station operation considering market dynamics and grid interaction. (2025). Li, LI ; Motlagh, Saheb Ghanbari ; Oladigbolu, Jamiu.
   In: Applied Energy.
   RePEc:eee:appene:v:392:y:2025:i:c:s0306261925007883.

   Full description at Econpapers || Download paper

5. Improving the learning process of deep reinforcement learning agents operating in collective heating environments. (2025). Verhaert, Ivan ; Hellinckx, Peter ; Huybrechts, Thomas ; Kabbara, Zakarya ; Houben, Pieter Jan ; Ghane, Sara ; Jacobs, Stef.
   In: Applied Energy.
   RePEc:eee:appene:v:384:y:2025:i:c:s0306261925001503.

   Full description at Econpapers || Download paper

6. Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems. (2025). Shang, Juan ; Ruan, Yingjun ; Xu, Tingting ; Liu, Mingzhe ; Chen, Wei-An ; Otomo, Junichiro ; Yamate, Shun ; Hu, Zehuan ; Gao, Yuan.
   In: Applied Energy.
   RePEc:eee:appene:v:384:y:2025:i:c:s0306261925001175.

   Full description at Econpapers || Download paper

7. Deep transfer learning strategy based on TimesBlock-CDAN for predicting thermal environment and air conditioner energy consumption in residential buildings. (2025). Hu, Zehuan ; Sun, Luning ; Imaizumi, Taiji ; Mae, Masayuki.
   In: Applied Energy.
   RePEc:eee:appene:v:381:y:2025:i:c:s0306261924025728.

   Full description at Econpapers || Download paper

8. AlphaDataCenterCooling: A virtual testbed for evaluating operational strategies in data center cooling plants. (2025). Zheng, Wanfu ; Wu, SI ; Li, Dingqian ; Yue, Shang ; Yang, PU ; Chen, Guanghao ; Wang, Zhe.
   In: Applied Energy.
   RePEc:eee:appene:v:380:y:2025:i:c:s030626192402484x.

   Full description at Econpapers || Download paper

9. A systematic review of predictive, optimization, and smart control strategies for hydrogen-based building heating systems. (2025). Pozarlik, Artur ; Acar, Canan ; Kaabinejadian, Amirreza.
   In: Applied Energy.
   RePEc:eee:appene:v:379:y:2025:i:c:s030626192402378x.

   Full description at Econpapers || Download paper

10. A dynamic coordination of microgrids. (2025). Adam, Ali A ; Elnady, A ; Naidoo, Raj M ; Bansal, Ramesh C ; Siti, Mukwanga W ; Mbungu, Nsilulu T ; Hamid, Abdul-Kadir ; Abokhali, Ahmed G.
    In: Applied Energy.
    RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924018695.

    Full description at Econpapers || Download paper

11. Safe deep reinforcement learning for building energy management. (2025). Zhu, Xiuli ; Huang, Renke ; Wang, Peng ; Li, Ning ; Arroyo, Javier.
    In: Applied Energy.
    RePEc:eee:appene:v:377:y:2025:i:pa:s0306261924017112.

    Full description at Econpapers || Download paper

12. Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort. (2024). Tabaa, Mohamed ; Hachimi, Hanaa ; Chegari, Badr ; Azzi, Amal.
    In: Sustainability.
    RePEc:gam:jsusta:v:16:y:2024:i:5:p:2154-:d:1351521.

    Full description at Econpapers || Download paper

13. A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process. (2024). Wang, Tianzheng ; Tang, Jian ; Tian, Hao.
    In: Sustainability.
    RePEc:gam:jsusta:v:16:y:2024:i:17:p:7650-:d:1470554.

    Full description at Econpapers || Download paper

14. Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions. (2024). Gkelios, Socratis ; Michailidis, Panagiotis ; Kosmatopoulos, Elias.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:3:p:570-:d:1325697.

    Full description at Econpapers || Download paper

15. Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions. (2024). Levron, Yoash ; Keren, Sarah ; Katzir, Liran ; Belikov, Juri ; MacHlev, Ram ; Naveh, Sivan Kaully ; Segev, Elior ; Balabanov, Alexander ; Ginzburg-Ganz, Elinor.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:21:p:5307-:d:1506486.

    Full description at Econpapers || Download paper

16. Multi-Agent Reinforcement Learning for Smart Community Energy Management. (2024). Li, Jie ; Wang, Ning ; Wilk, Patrick.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:20:p:5211-:d:1502573.

    Full description at Econpapers || Download paper

17. Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator. (2024). Capozzoli, Alfonso ; Yaghoubi, Ali Reza ; Fop, Davide.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:20:p:5117-:d:1498929.

    Full description at Econpapers || Download paper

18. Multi-Layer Energy Management and Strategy Learning for Microgrids: A Proximal Policy Optimization Approach. (2024). Zhang, Yuhao ; Fang, Xiaohan ; He, Shuping ; Hong, Peng ; Tan, DI.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:16:p:3990-:d:1454689.

    Full description at Econpapers || Download paper

19. Decision Tree Variations and Online Tuning for Real-Time Control of a Building in a Two-Stage Management Strategy. (2024). Rigo-Mariani, Remy ; Yakub, Alim.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:11:p:2730-:d:1408250.

    Full description at Econpapers || Download paper

20. Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC. (2024). Gao, Renjing ; Wang, QI ; Zhou, Guangli.
    In: Energy.
    RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007837.

    Full description at Econpapers || Download paper

21. An adaptive switching control model for air conditioning systems based on information completeness. (2024). Tian, Zhe ; Ding, Yan ; Yang, Xiaochen ; Zhang, Haozheng ; Huang, Chen.
    In: Applied Energy.
    RePEc:eee:appene:v:375:y:2024:i:c:s0306261924013874.

    Full description at Econpapers || Download paper

22. Long-term experimental evaluation and comparison of advanced controls for HVAC systems. (2024). Wang, Xuezheng ; Dong, Bing.
    In: Applied Energy.
    RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010894.

    Full description at Econpapers || Download paper

23. Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty. (2024). Hu, Zehuan ; Gao, Yuan ; Imaizumi, Taiji ; Mae, Masayuki ; Sun, Luning.
    In: Applied Energy.
    RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010353.

    Full description at Econpapers || Download paper

24. A novel joint energy and demand management system for smart houses based on model predictive control, hybrid storage system and quality of experience concepts. (2024). da Costa, Paulo Renato ; Normey-Rico, Julio Elias ; Forte, Jose Diogo ; Gouvea, Guilherme Nascimento ; Naspolini, Amir.
    In: Applied Energy.
    RePEc:eee:appene:v:369:y:2024:i:c:s0306261924008493.

    Full description at Econpapers || Download paper

25. Establishment of LCZ-based urban building energy consumption dataset in hot and humid subtropical regions through a bottom-up method. (2024). Tian, Xiaoyu ; Liu, Liru ; Huang, Jiahao ; Zhang, Hanwen.
    In: Applied Energy.
    RePEc:eee:appene:v:368:y:2024:i:c:s0306261924008742.

    Full description at Econpapers || Download paper

26. Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning. (2024). Wang, Zixuan ; Ran, YI ; Li, Yanxue ; Xu, Yang ; Xiao, FU.
    In: Applied Energy.
    RePEc:eee:appene:v:367:y:2024:i:c:s0306261924007979.

    Full description at Econpapers || Download paper

27. Bi-level optimal operations for grid operator and low-carbon building prosumers with peer-to-peer energy sharing. (2024). Wang, Xiaoyu ; Jin, Xiaolong ; Yu, Xiaodan ; Wei, Wei ; Liang, Shuo ; Jia, Hongjie ; Mu, Yunfei.
    In: Applied Energy.
    RePEc:eee:appene:v:359:y:2024:i:c:s0306261924001065.

    Full description at Econpapers || Download paper

28. Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads. (2024). Feng, Meili ; Shi, Linyu ; Zhao, Jing ; Wang, Hongbin ; Li, Haonan ; Liu, Dehan ; Yang, Zilan ; Mi, Yumiao ; Hutagaol, Timothy Joseph.
    In: Applied Energy.
    RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017944.

    Full description at Econpapers || Download paper

29. Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach. (2024). Chen, Xiwen ; Vital, Natan ; Wang, Hao ; Duffy, Edward ; Razi, Abolfazl.
    In: Applied Energy.
    RePEc:eee:appene:v:356:y:2024:i:c:s030626192301718x.

    Full description at Econpapers || Download paper

30. How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system. (2023). Oneill, Zheng ; Fu, Yangyang ; Adetola, Veronica ; Xu, Shichao ; Zhu, QI.
    In: Energy.
    RePEc:eee:energy:v:273:y:2023:i:c:s036054422300467x.

    Full description at Econpapers || Download paper

31. Optimization of building demand flexibility using reinforcement learning and rule-based expert systems. (2023). Ma, Zhenjun ; Zhou, Xinlei ; Xue, Shan ; Du, Han.
    In: Applied Energy.
    RePEc:eee:appene:v:350:y:2023:i:c:s030626192301156x.

    Full description at Econpapers || Download paper

32. MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities. (2023). Nagy, Zoltan ; Sankaranarayanan, Siva ; Nweye, Kingsley.
    In: Applied Energy.
    RePEc:eee:appene:v:346:y:2023:i:c:s0306261923006876.

    Full description at Econpapers || Download paper

33. Energy saving and indoor temperature control for an office building using tube-based robust model predictive control. (2023). Gao, Yuan ; Akashi, Yasunori ; Miyata, Shohei.
    In: Applied Energy.
    RePEc:eee:appene:v:341:y:2023:i:c:s0306261923004701.

    Full description at Econpapers || Download paper

34. The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Buildings. (2022). Cieliski, Krzysztof ; Bielskus, Jonas ; Piotrowska-Woroniak, Joanna.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:8:p:2758-:d:790052.

    Full description at Econpapers || Download paper

35. Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types. (2022). Shaqour, Ayas ; Hagishima, Aya.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:22:p:8663-:d:976978.

    Full description at Econpapers || Download paper

36. Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system. (2022). Matsunami, Yuki ; Gao, Yuan ; Akashi, Yasunori ; Miyata, Shohei.
    In: Applied Energy.
    RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012788.

    Full description at Econpapers || Download paper

37. A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings. (2022). Peng, Yuzhen ; Lei, Yue ; Hasama, Takamasa ; Zhan, Sicheng ; Ono, Eikichi ; Chong, Adrian ; Zhang, Zhiang.
    In: Applied Energy.
    RePEc:eee:appene:v:324:y:2022:i:c:s0306261922010297.

    Full description at Econpapers || Download paper

Report date: 2025-10-01 02:35:30 || Missing content? Let us know