Coordinated energy management for a cluster of buildings through deep reinforcement learning

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Generalizable Data-Driven Building Operation Control Methods for Scalable Cross-Building Applications

Fan, C., Lei, Y., Piscitelli, M. S., & Mo, J. (2025) · Energy Use · doi:10.59717/ipj.energy-use.2025.100008 · Cited by 7 · see citing works

Digital twin and AI-driven smart adaptive walls for energy, comfort and carbon optimization in intelligent green buildings

Abuhussain, M. A. (2026) · Energy · doi:10.1016/j.energy.2026.140990 · Cited by 7 · see citing works

Data-Driven Flexibility-oriented Energy Management Strategy for Building Cluster Meso Energy Hubs

Nozarian, M., Hajizadeh, A., & Fereidunian, A. (2023) · doi:10.1109/icsmartgrid58556.2023.10170914 · Cited by 7 · see citing works

Adaptive home energy management based on PI-DRA-PPO for integrated electricity, hydrogen, and heat storage systems

Liu, M., Bu, S., Zhu, Z., & Zhou, B. (2026) · Applied Energy · doi:10.1016/j.apenergy.2025.127257 · Cited by 6 · see citing works

A Privacy-Preserving RL-Based Secure Charging Coordinator Using Efficient FL for Smart Grid Home Batteries

Elshazly, A. A., Elgarhy, I., Mahmoud, M. M. E. A., Ibrahem, M. I., & Alsabaan, M. (2025) · Energies · doi:10.3390/en18040961 · Cited by 6 · see citing works

Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids

Wu, M., He, Q., Liu, Y., Zhang, Z., Shi, Z., & He, Y. (2022) · Sustainability · doi:10.3390/su142113939 · Cited by 6 · see citing works

A framework for the design of representative neighborhoods for energy flexibility assessment in CityLearn

Nweye, K., Kaspar, K., Buscemi, G., Pinto, G., Li, H., Hong, T., Ouf, M. M., Capozzoli, A., & Nagy, Z. (2023) · Building Simulation Conference proceedings · doi:10.26868/25222708.2023.1404 · Cited by 6 · see citing works

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 · see citing works

Multi-agent and Reinforcement Learning Schemes for Demand Response Estimation in Distributed Microgrids

Ikram, M., Ahmed, S., Marwat, S. N. K., & Nasir, M. (2023) · International Conference on Energy Conservation and Efficiency (ICECE) · doi:10.1109/icece58062.2023.10092514 · Cited by 5 · see citing works

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

Gokhale, G., Tiben, N., Verwee, M.-S., Lahariya, M., Claessens, B., & Develder, C. (2023) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3600100.3625681 · Cited by 5 · see citing works

SWOAM: Swarm optimized agents for energy management in grid-interactive connected buildings

Tungom, C. E., Wang, H., Beata, K., & Niu, B. (2024) · Energy · doi:10.1016/j.energy.2024.131399 · Cited by 5 · see citing works

Addressing partial observability in reinforcement learning for energy management

Biemann, M., Liu, X., Zeng, Y., & Huang, L. (2021) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3486611.3488730 · Cited by 5 · see citing works

Pareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles

Dengiz, T., Raith, A., Kleinebrahm, M., Vogl, J., & Fïchtner, W. (2025) · Energy · doi:10.1016/j.energy.2025.138063 · Cited by 4 · see citing works

Community Energy Management Using MARL: Synergy of Price-Based and Incentive-Based Demand Response

Hashemnezhad, M., Delkhosh, H., Shahabi, A., & Moghaddam, M. P. (2024) · doi:10.1109/icee63041.2024.10667866 · Cited by 4 · see citing works

MuFlex: A scalable, physics-based platform for multi-building flexibility analysis and coordination

Wu, Z., Korolija, I., & Tang, R. (2026) · Energy · doi:10.1016/j.energy.2026.140565 · Cited by 3 · see citing works

Smart Building–Grid Interaction in Urban Energy Transitions: A Taxonomy of Key Performance Indicators and Enabling Technologies

Toosi, R. A., Gholamzadehmir, M., & Toosi, H. A. (2025) · Urban Science · doi:10.3390/urbansci9110483 · Cited by 3 · see citing works

Model-Free Deep Reinforcement Learning for Adaptive Supply Temperature Control in Collective Space Heating Systems

Ghane, S., Jacobs, S., Huybrechts, T., Hellinckx, P., Mercelis, S., Verhaert, I., & Mannens, E. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3709010 · Cited by 3 · see citing works

Improving the learning process of deep reinforcement learning agents operating in collective heating environments

Jacobs, S., Ghane, S., Houben, P. J., Kabbara, Z., Huybrechts, T., Hellinckx, P., & Verhaert, I. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125420 · Cited by 3 · see citing works

Recent progress in the energy flexibility of building clusters: A systematic review

Zhu, K., Wang, J., Santamouris, M., Yu, D., Xiang, Y., Tian, S., Yang, Y., & Shi, X. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117458 · Cited by 2 · see citing works

Reinforcement learning for residential energy storage management at the neighborhood scale: A multi-benchmark evaluation

Kaspar, K., Hussain, S., Menon, R. P., Ouf, M. M., & Eicker, U. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100697 · Cited by 2 · see citing works

Flexibility of building virtual energy storage: a systematic quantitative review of modeling and controlling heating and cooling systems

Xu, W., Ruan, Y., Wu, Z., Xu, T., Yao, Y., Meng, H., Wang, C., Liu, W. D., & Xia, Y. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128047 · Cited by 2 · see citing works

Reinforcement Learning for Optimizing Renewable Energy Utilization in Smart Grids: Recent Advances in Power Grids, Microgrids, and Building Energy Systems

Michailidis, P., Minelli, F., Çoban, H. H., Michailidis, I., & Kosmatopoulos, E. B. (2026) · Infrastructures · doi:10.3390/infrastructures11070240 · Cited by 2 · see citing works

Data-driven energy management of a neighborhood with renewable energy sources

Yang, Y., Brozovsky, J. G., Liu, P., & Goia, F. (2023) · Building Simulation Conference proceedings · doi:10.26868/25222708.2023.1490 · Cited by 2 · see citing works

Research on Advanced Control Strategies for Building Energy Management Systems to Optimize Energy Consumption in Commercial Buildings by Using DRL-PC Algorithm

Alam, N. N., Faiz, R., Shatil, A. H., Ohid, M. A., Akib, M. A. A., & Khan, M. Y. A. (2025) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest63960.2025.10914385 · Cited by 1 · see citing works

A training workbench based on transient building models for creating intelligent energy operators

Mulero, R., Garay-Martinez, R., Mendialdua, I., & Arregi, B. (2025) · Data-Centric Engineering · doi:10.1017/dce.2025.10016 · Cited by 1 · see citing works

Application analysis of hybrid heat pump heating systems optimized with deep reinforcement learning control

Liu, H., Fang, Z., Yang, S., Mao, C., Wang, Z., Chen, Z., Song, Y., & Duan, P. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.114024 · Cited by 1 · see citing works

Adaptive AI in Smart Grid: A Continual Reinforcement Learning Framework for Cyber-Physical Systems

Xiong, H., & Yu, J. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161096 · Cited by 1 · see citing works

Enhancing grid-friendliness in PV building clusters via a coordinated control strategy integrating bidirectional electric vehicle charging

Du, A., Jiang, H., Yuan, L., & Gao, D.-C. (2026) · Energy · doi:10.1016/j.energy.2026.140752 · Cited by 1 · see citing works

Control strategies for district heating and cooling systems: a comprehensive review across production, distribution, and end-user levels

Monghasemi, N., Vadiee, A., Vouros, S., & Kyprianidis, K. (2026) · Energy Informatics · doi:10.1186/s42162-026-00650-9 · Cited by 1 · see citing works

Reinforcement Learning-Based Energy Management System for Optimal Distributed Energy Resources Control Policies in Smart MicrogridsPreprint

Hajaligol, P., Nweye, K., Aghaei, M., Moazami, A., Najafi, B., & Nagy, Z. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4657494 · Cited by 1 · see citing works

Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering

Sayers, W. (2021) · Advances in e-business research series · doi:10.4018/978-1-7998-7712-7.ch004

Reducing Peak Electricity Demands of a Cluster of Buildings with Multi-Agent Reinforcement Learning

Balwant, M. K., Basa, S. R., & Misra, R. (2023) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-15175-0_25

Model-Free Online Reinforced Learning Control for Responsive Building EnvelopePreprint

He, Y., Fahimi, F., & Zhou, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4599451

Autonomous Micro-Grids: A Reinforcement Learning-Based Energy Management Model in Smart Cities

Özkan, E., Kök, İ., & Özdemi̇r, S. (2023) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc58260.2023.10323891

Predictions and Policies on Carbon Footprint Release Data at the College of Vocational Studies, IPB University (SV IPB) Based on the Contribution of Campus Operational Activities

Hidayat, A., Martini, R., Ristianingrum, A., Priatna, W. B., Indrawan, P., Manalu, D. S. T., Wiraguna, E., Dewi, H., Dewi, S. P., Pratama, A. J., et al. (2024) · Environment and Ecology Research · doi:10.13189/eer.2024.120105

Reinforcement Learning for Residential Heat Pump OperationPreprint

Schmitz, S., Brucke, K., Kasturi, P., Ansari, E., & Klement, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4715980

Enhancing Self-Consumption Ratio in a Smart Microgrid by Applying a Reinforcement Learning-Based Energy Management SystemPreprint

Hajaligol, P., Nweye, K., Aghaei, M., Najafi, B., Moazami, A., & Nagy, Z. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4852112

Data Driven Multi-Agent Deep Reinforcement Learning Based Operational Optimization of Industrial Aerodynamic System

Huang, Y., Zhong, L., Liu, Y., Zhao, J., & Wang, W. (2024) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls61622.2024.10606700

Machine Learning Models are Used to Analyze the Effectiveness of Daily Residential Area Energy Consumption

Aggarwal, K., Selvi, D. M. K., Rayabharapu, V. K., & Chakradhar, K. S. (2024) · doi:10.1002/9781394231522.ch13

Traffic engineering

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A Centralized Deep Reinforcement Learning Method for Demand Response in Building Clusters

Liu, S., Yang, J., & Gao, D.-C. (2025) · International Conference on Smart Grid and Energy (ICSGE) · doi:10.1109/icsge68440.2025.11441914

Deep Reinforcement Learning based Energy Management System for Controlling Storage Systems in a Cluster of Buildings: Cypriot use case

Hajialigol, P., Papadopoulos, P., Kyprianou, I., & Aghaei, M. (2024) · International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED) · doi:10.1109/synergymed62435.2024.10799497 · Cited by 3 · see citing works

Flexible Power Load Control Based on Hierarchical Multi-Agent Reinforcement Learning

Wang, Y., Wang, X., Zhu, Q., Yuan, Q., Luo, G., & Li, J. (2024) · International Conference on Power and Renewable Energy (ICPRE) · doi:10.1109/icpre62586.2024.10768325 · Cited by 1 · see citing works

Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings

Deltetto, D., Coraci, D., Pinto, G., Piscitelli, M. S., & Capozzoli, A. (2021) · Energies · doi:10.3390/en14102933 · Cited by 35 · see citing works