Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268

Technical Note: Q-Learning

Watkins, C., & Dayan, P. (1992) · Machine-mediated learning · doi:10.1023/a:1022676722315 · Cited by 14,277

Playing Atari with Deep Reinforcement Learning

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408

Q-learning

Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083

Reinforcement Learning and Dynamic Programming Using Function Approximators

Buşoniu, L., Babuška, R., Schutter, B. D., & Ernst, D. (2010) · CRC Press eBooks · doi:10.1201/9781439821091 · Cited by 919

Reinforcement learning for demand response: A review of algorithms and modeling techniques

Vázquez-Canteli, J. R., & Nagy, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.11.002 · Cited by 883

On-Line Building Energy Optimization Using Deep Reinforcement Learning

Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2834219 · Cited by 649

The Neural Network House: An Environment that Adapts to its Inhabitants

Mozer, M. C. (1998) · Cited by 500

Distributed reinforcement learning energy management approach in multiple residential energy hubs

Ahrarinouri, M., Rastegar, M., Karami, K., & Seifi, A. R. (2022) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2022.100795 · Cited by 26

A multi-discipline predictive intelligent control method for maintaining the thermal comfort on indoor environment

Qin, H., & Wang, X. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108299 · Cited by 24

Energy transition roadmap towards net-zero communities: A case study in Japan

Li, Y., Wang, Y., Zhou, R., Qian, H., Gao, W., & Zhou, W. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.105045 · Cited by 24

Towards a Service-Oriented Architecture for the Energy Efficiency of Buildings: A Systematic Review

Mendoza, L., Calderón, H., Vargas-Lombardo, M., Gomez-Pulido, J. M., & Castillo, J. L. (2021) · IEEE Access · doi:10.1109/access.2021.3057543 · Cited by 24

Deep neural networks as surrogate models for urban energy simulations

Vázquez-Canteli, J. R., Dilsiz, A. D., Brown, J. D., & Nagy, Z. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1343/1/012002 · Cited by 24

An Open-Source Virtual Testbed for a Real Net-Zero Energy Community

Huang, S., Wang, J., Fu, Y., Zuo, W., Hinkelman, K., Kaiser, R., He, D., & Vrabie, D. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103255 · Cited by 23

Trombe wall thermal performance: Data mining techniques for indoor temperatures and heat flux forecasting

Briga-Sá, A., Leitão, D., Boaventura‐Cunha, J., & Martins, F. F. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111407 · Cited by 24

A reinforcement learning approach for thermostat setpoint preference learning

Elehwany, H., Ouf, M. M., Gunay, B., Cotrufo, N., & Venne, J.-S. (2023) · Building Simulation · doi:10.1007/s12273-023-1056-7 · Cited by 22

Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

Tien, P. W., Wei, S., Calautit, J. K., Darkwa, J., & Wood, C. J. (2020) · Journal of Sustainable Development of Energy Water and Environment Systems · doi:10.13044/j.sdewes.d8.0378 · Cited by 22

Monitoring data-driven Reinforcement Learning controller training: A comparative study of different training strategies for a real-world energy system

Schreiber, T., Netsch, C., Baranski, M., & Müller, D. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110856 · Cited by 26

Solar potential of urban forms of a cold semi-arid city in Algeria in the present and future climate

Bensehla, S., Lazri, Y., & Brito, M. C. (2021) · Energy Sustainable Development/Energy for sustainable development · doi:10.1016/j.esd.2021.04.004 · Cited by 22

Data Driven Q-Learning for Commercial HVAC Control

Faddel, S., Tian, G., Zhou, Q., & Aburub, H. (2020) · IEEE SoutheastCon · doi:10.1109/southeastcon44009.2020.9249737 · Cited by 21

Systematic review of deep learning and machine learning for building energyPreprint

Ardabili, S., Abdilalizadeh, L., Makó, C., Torok, B., & Mosavi, A. H. (2022) · doi:10.31219/osf.io/fxtmz · Cited by 21

Exploring Decomposition for Solving Pattern Mining Problems

Djenouri, Y., Lin, J. C., Nørvåg, K., Ramampiaro, H., & Yu, P. S. (2021) · ACM Transactions on Management Information Systems · doi:10.1145/3439771 · Cited by 23

Building simulation in adaptive training of machine learning models

Amini, H., Alanne, K., & Kosonen, R. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105564 · Cited by 19

Machine Learning for Identifying Group Trajectory Outliers

Belhadi, A., Djenouri, Y., Djenouri, D., Michalak, T., & Lin, J. C. (2021) · ACM Transactions on Management Information Systems · doi:10.1145/3430195 · Cited by 19

The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities

Gallo, A., & Capozzoli, A. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115043 · Cited by 17

Artificial Jellyfish Optimization with Deep-Learning-Driven Decision Support System for Energy Management in Smart Cities

Alqarafi, A., Alsolai, H., Alzahrani, J. S., Negm, N., Alharbi, L. A., Duhayyim, M. A., Mohsen, H., Al-Shabi, M. A., & Al‐Wesabi, F. N. (2022) · Applied Sciences · doi:10.3390/app12157457 · Cited by 17

Optimal control of a solar-driven seasonal sorption storage system through deep reinforcement learning

Crespo, A., Gibert, D., de Gracia, Á., & Fernández, C. C. (2023) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.121905 · Cited by 14

Optimizing the hyper-parameters of deep reinforcement learning for building control

Li, S., Su, S., & Lin, X. (2025) · Building Simulation · doi:10.1007/s12273-025-1233-y · Cited by 13

Optimized Mathematical Model for Energy Efficient Construction Management in Smart Cities Using Building Information Modeling

Wang, J. (2022) · Strategic Planning for Energy and the Environment · doi:10.13052/spee1048-5236.4113 · Cited by 12

A bidirectional and low-frequency energy harvester for collecting human crowd energy in shopping malls

Ren, L., Luo, Y., Lu, G., Cong, M., Wang, X., Wang, K., Guo, Z., & Tan, Y. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.115046 · Cited by 12

Methodology to determine residential heating-based flexibility potential in electricity distribution networks

Haakana, J., Haapaniemi, J., Vilppo, J., & Lassila, J. (2022) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2022.100961 · Cited by 11

Prospective study of a novel heat pump system with solar energy spectral beam splitting

Hao, X., Zhou, Y., Wang, Q., Gao, N., Wang, X., & Chen, G. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.119209 · Cited by 10

USING DEEP LEARNING TO OPTIMIZE HVAC SYSTEMS IN RESIDENTIAL BUILDINGS

Quang, T. V., & Phuong, N. L. (2024) · Journal of Green Building · doi:10.3992/jgb.19.1.29 · Cited by 10

Improving the Ambient Temperature Control Performance in Smart Homes and Buildings

Fontes, F., Antão, R., Mota, A. M., & Pedreiras, P. (2021) · Sensors · doi:10.3390/s21020423 · Cited by 10

THE BENEFITS OF ARTIFICIAL INTELLIGENCE IN CONSTRUCTION PROJECTS

Elfargani, M. S. Y., & ., E. (2022) · Acta Informatica Malaysia · doi:10.26480/aim.02.2022.47.51 · Cited by 9

Knowledge-guided reinforcement learning for HVAC controls and energy saving through an efficient simulation framework

Lv, Z., Peng, X., Zhang, Y., Zhang, B., Lin, B., Yuan, C., & Yang, G. (2025) · Energy · doi:10.1016/j.energy.2025.139009 · Cited by 9

Synthetic data generation and energy consumption prediction in district building energy modeling

Bolluk, S., Aydoğan, R., Sefer, E., Kalaycıoğlu, E., & Seyıs, S. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116716 · Cited by 8

Automation of Russian industry as an indispensable condition for sustainable economic development in the digital environment

Avdeeva, E., Averina, T., Davydova, T. E., & Zhutaeva, E. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/862/4/042041 · Cited by 8

Unraveling energy hub dynamics and political economy toward sustainable urban energy: A multifaceted examination of policy decision making and implementation

Li, Y., Chen, W., & Song, B. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.105159 · Cited by 8

Applications of AI and IoT for Smart Cities

Kannammal, A., & Chandia, S. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815136449123010014 · Cited by 8

Application of Artificial Intelligence in Air Conditioning Systems

Myat, A. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.107379 · Cited by 8

Sensorless Air Flow Control in an HVAC System through Deep Learning

Son, J., & Kim, H. (2019) · Applied Sciences · doi:10.3390/app9163293 · Cited by 8

Adaptive transfer reinforcement learning (TRL) for cooling water systems with uniform agent design and multi-agent coordination

Wang, Z., & Pang, Z. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116071 · Cited by 8

On the Performance of Data-Driven Reinforcement Learning for Commercial HVAC Control

Faddel, S., Tian, G., Zhou, Q., & Aburub, H. (2020) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias44978.2020.9334865 · Cited by 7

Multi-agent reinforcement learning for adaptive demand response in smart cities

Vázquez-Canteli, J. R., Deetjen, T. A., Henze, G. P., Kämpf, J. H., & Nagy, Z. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1343/1/012058 · Cited by 9

Deep Reinforcement Learning for Sustainable Urban Mobility: A Bibliometric and Empirical Review

Jamal, S., Siddiqui, F., Alam, M. A., Mursaleen, M., Zafar, S., & Naaz, S. (2026) · Sensors · doi:10.3390/s26020376 · Cited by 7

MORL-SGF: A Governance-Aware Multi-Objective Reinforcement Learning Framework with Digital Twin Policy Validation for Sustainable Smart Cities

Alharbi, S. T. (2026) · Systems · doi:10.3390/systems14030294 · Cited by 7

Factors of sustainable development of energy technologies in the formation of digital economy

Авдеева, Е., Davydova, T. E., Belyantseva, O., & Makeeva, T. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202016409045 · Cited by 6

Multi-Scalar Urban Digital Twin Design: Architecture and OpenUSD Standards Based Methodology

(2023) · doi:10.56831/psen-04-100 · Cited by 6

An Online Home Energy Management System using Q-Learning and Deep Q-Learning

İzmitligil, H., & Karamancıoğlu, A. (2024) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2024.101005 · Cited by 6

The Role of Artificial Intelligence in Smart Cities: Systematic Literature Review

Dominiković, I., Ćukušić, M., & Jadrić, M. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-77417-2_5 · Cited by 6

Urban energy management system based on intelligent linker

Sun, H., Caluyo, F. S., Ocampo, A. L. P. D., Hernandez, R., & Sarmiento, J. (2024) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt2024.915 · Cited by 6

Artificial Intelligence Applied to the Management and Operation of Solar Systems

Ghoshal, N., & Tripathy, B. K. (2024) · Wiley eBooks · doi:10.1002/9781394249374.ch19 · Cited by 6

Intelligent System Application to Monitor the Smart City Building Lighting

Chen, T.-C., Dwijendra, N. K. A., Singhal, S., Sivaraman, R., & Mamdouh, A. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035418 · Cited by 5

Development Strategy Based on Combination Typologies of Building Carbon Emissions and Urban Vibrancy—A Multi-Sourced Data-Driven Approach in Beijing, China

Xia, J., Wang, J., & Lai, Y. (2024) · Land · doi:10.3390/land13071062 · Cited by 5

Potential evaluation of AI-based surrogate models to find the robust and optimal method for thermal performance assessment of desiccant air-conditioning systems

Hussain, I., Sajjad, U., Bhat, S. A., Abbas, N., Ali, A., Sultan, M. A., Ali, H. M., Hamid, K., Yan, W., & Rubab, N. (2024) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2024.100782 · Cited by 5

Enhancing Public Building Energy Efficiency Through Digital Twin Modeling and Iterative Simulation: A Sustainable Approach

Ullah, Z., Aziz, M. O., Gruosso, G., Ullah, R., Ahmad, N., & Scacciante, A. (2024) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce63537.2024.10823633 · Cited by 5

A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling

Anagnostis, A., Moustakidis, S., Papageorgiou, E. I., & Bochtis, D. (2022) · Energies · doi:10.3390/en15061959 · Cited by 5

Quantifying Sustainability and Landscape Performance: A Smart Devices Assisted Alternative Framework

Shen, Z., Peng, X., Du, C., & Kim, M. (2023) · Sustainability · doi:10.3390/su151713239 · Cited by 5

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

Hu, J., & Xu, Z. (2023) · Journal of Information Systems Engineering & Management · doi:10.55267/iadt.07.14037 · Cited by 5

A Transfer Learning Strategy for Improving the Data Efficiency of Deep Reinforcement Learning Control in Smart Buildings

Amasyali, K., Liu, Y., & Zandi, H. (2024) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt59692.2024.10454120 · Cited by 5

Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy Models

Lee, J., Huang, S., Rahman, A., Smith, A. D., & Katipamula, S. (2020) · doi:10.1145/3427773.3427873 · Cited by 5

The impact of deep learning–based equipment usage detection on building energy demand estimation

Wei, S., Tien, P. W., Wu, Y., & Calautit, J. K. (2021) · Building Services Engineering Research and Technology · doi:10.1177/01436244211034737 · Cited by 5

Makf-Sr: Multi-Agent Adaptive Kalman Filtering-Based Successor Representations

Salimibeni, M., Malekzadeh, P., Mohammadi, A., Spachos, P., & Plataniotis, K. N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414597 · Cited by 5

Machine Learning in Urban Building Energy Modeling

Abbasabadi, N., & Ashayeri, M. (2024) · Wiley eBooks · doi:10.1002/9781394172092.ch2 · Cited by 6

DEEP LEARNING IN HEAT TRANSFER

Tamaddon-Jahromi, H. R., Chakshu, N. K., Thomas, H., & Nithiarasu, P. (2022) · Annual Reviews of Heat Transfer · doi:10.1615/annualrevheattransfer.2022042131 · Cited by 4

Algorithmic Innovations in Multi-Agent Reinforcement Learning: A Pathway for Smart Cities

Agbossou, I. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.113933 · Cited by 4

Reinforcement Learning Methodologies for Controlling Occupant Comfort in Buildings

Han, M., May, R., & Zhang, X. (2021) · Sustainable development goals series · doi:10.1007/978-981-16-2778-1_9 · Cited by 5

Explaining Deep Reinforcement Learning-Based Methods for Control of Building HVAC Systems

Jiménez-Raboso, J., Manjavacas, A., Campoy-Nieves, A., Molina-Solana, M., & Gómez‐Romero, J. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_13 · Cited by 4

A Demand-Response Approach for HVAC Systems Using Internet of Energy Concept

Tomin, N., Kolosok, I., Kurbatsky, V. G., & Korkina, E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-49390-4_20 · Cited by 4

Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones

Alamaniotis, M., & Alexiou, M. S. (2024) · Electronics · doi:10.3390/electronics13040762 · Cited by 4

Exploring the Evolution of Smart Cities: A Review on Energy, Mobility, and Waste Management Aspects

Bregoli, D., Liscio, M. C., Nabiti, N., & Sospiro, P. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.114330 · Cited by 4

Federated Learning in Secure Smart City Sensing

Gandhi, M., Singh, S. K., Ravikumar, R. N., & Vaghela, K. N. (2025) · Wiley eBooks · doi:10.1002/9781394314409.ch8 · Cited by 4

Improving the Efficiency of Environmental Temperature Control in Homes and Buildings

Kunelbayev, M., Amirgaliyev, Y. N., & Sundetov, T. (2022) · Energies · doi:10.3390/en15238839 · Cited by 3

Environmental and industrialization challenges of nanofluids

Shirazi, N. A., & Rahimpour, M. R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823996-4.00020-3 · Cited by 3

Prospects for the creation of smart homes using energy-saving wall ceramic materials

Turchenko, A., Davydova, T. E., & Spivak, I. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202016402029 · Cited by 2

Advances in the Study of Building Energy Consumption by Machine Learning Method

李, 继. (2020) · Computer Science and Application · doi:10.12677/csa.2020.105103 · Cited by 1

The Most Adjusted Predictive Models for Energy Costs

Martinho, V. J. P. D. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-54608-2_7 · Cited by 1

Applications of artificial neural networks in three agro-environmental systems: microalgae production, nutritional characterization of soils and meteorological variables management

Ortellado, B. M. F. (2019) · doi:10.35376/10324/39419 · Cited by 1

Infrastructural Data Visualization and Improved User Interfaces of Energy Consumption in Smart Cities

Selvaraj, P., Kanmani, P., Malleswari, T. Y. J. N., Burugari, V. K., & Mangalampalli, S. (2024) · Cyber-Physical Energy Systems · doi:10.1002/9781394173006.ch11 · Cited by 1

Desarrollo de una aplicación para la predicción de ingredientes y recetas de cocina por medio de TensorFlow y máquinas de soporte vectorial

Castaño, Y. Y. M., Castillo, L., Gómez, Ó. D. C., Buitrago-Carmona, F., & Giraldo, S. L. (2020) · Revista Ingenierías Universidad de Medellín · doi:10.22395/rium.v19n37a10

Identification of the leading research domains and grouping of articles on the smart city using text mining

Zdonek, D. (2020) · Scientific Papers of Silesian University of Technology Organization and Management Series · doi:10.29119/1641-3466.2020.148.62

Data–model integration layer

Fang, Z., Lu, Q., Xie, X., Parlikad, A. K., Schooling, J. M., & Pitt, M. (2022) · doi:10.1680/dtbe.65802.161

Calculation of temperature data from an automatic solar heat supply system

Kunelbayev, M., Ixanov, S. S., Иманбек, Б., Omarov, R., & Dauren, O. (2023) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijpeds.v14.i1.pp622-629

Application of Deep Reinforcement Learning to Temperature Control of a Chamber for Ultra-precision Machines

Kim, B.-S., & Ro, S.-K. (2023) · Journal of the Korean Society for Precision Engineering · doi:10.7736/jkspe.022.124

Building Design from an Environmental Perspective: A Review on Sustainable Building Design Models and Analysis of their Application in Practice

Agarwal, A. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem25508

Dynamic Involvement of Deep Learning and Big Data in Smart Cities

Shah, N., Kapoor, A., Gupta, N., Agarwal, V., & Jindal, M. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815136173123010007

Exploring the Potential of Reinforcement Learning for Applications in Hydrogen Energy Systems

Li, Y., & Miao, Y. (2023) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc58545.2023.10328000

Inovação no setor público para a promoção de qualidade de vida nas cidades: compreendendo a literatura da inovação

Maia, N. C., do Nascimento, A. B. F. M., & Maracajá, K. F. B. (2024) · Caderno Pedagógico · doi:10.54033/cadpedv21n12-247

Behavioural Aspects and Human Factors

Samad, W. A., Badran, A., & Azar, E. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009471527.015

Toward Smart Buildings and Communities in the Gulf States

Abulibdeh, A. O., Zaidan, E., & Jafari, M. A. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009471527.017

Leveraging data analytics and ML for enhanced renewable energy resource management

Saikia, A. P., Dey, K., Gill, A., & Gantra, A. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02668-2

Optimizing HVACR System Efficiency and Comfort Levels Using Machine Learning-Based Control Methods

Dawood, S. M., Homod, R. Z., & Hatami, A. (2025) · Tikrit Journal of Engineering Sciences · doi:10.25130/tjes.32.2.25

A Review of Deep Learning Approaches in Smart City Applications

Paramasivan, S. K., & Subbiah, S. S. (2025) · doi:10.1002/9781394233823.ch22

Systematic evaluation of deep Q-networks for multi-chiller system reinforcement learning control

Cha, J. H., Chae, J. W., Yeon, S. H., Park, J. K., & Lee, K. H. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.128578

Improving Predicted Mean Vote with Inversely Determined Metabolic Rate

Zhang, S., Jiang, J., Cheng, Y. C., & Lin, Z. (2025) · Indoor Environment and Sustainable Building/Indoor environment and sustainable building · doi:10.1007/978-981-95-6288-6_4

Thermal Comfort Control with Energy Constraints in Smart Buildings

Khalil, M. (2022) · theses.fr (ABES) · doi:10.70675/91637969z4070z4816z8ce3zbfe93506a6f7

Multi-objective optimization for multi-zone HVAC system via physics-integrated predictive control framework with CFD validationPreprint

Hou, F., Cheng, J. C. P., Huang, C., Li, A., Kwok, H. L., & Wu, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6583482

Interpretable Federated Reinforcement Learning for Large-Scale Distributed HVAC Control

Genetti, S., Micheli, E., Custode, L. L., & Iacca, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-23607-4_1

Multi-objective optimization for multi-zone HVAC system via physics-integrated predictive control framework with CFD-assisted visualization

Hou, F., Cheng, J. C. P., Huang, C., Li, A., Kwok, H. L., & Wu, Z. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115094

Whale and ant colony optimization for energy-efficient IoT-WSN clustering and routing in smart cities

Hamidi, H., & Ardebily, M. S. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01299-w

Navigating the evolving strategies and tools for building energy efficiency

Atta, I., Bakhoum, E. S., & Marzouk, M. M. (2026) · Energy Efficiency · doi:10.1007/s12053-026-10477-8

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Villano, F., Mauro, G. M., & Pedace, A. (2024) · Thermo · doi:10.3390/thermo4010008 · Cited by 63

A simulation study on energy optimization in building control with reinforcement learning

Bolt, P., Ziebart, V., Jaeger, C., Schmid, N., Stadelmann, T., & Füchslin, R. M. (2024) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-31129

Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

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