Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings

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MARLISA

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MARCO - Multi-Agent Reinforcement learning based COntrol of building HVAC systems

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Grid-Interactive Efficient Buildings Technical Report Series: Whole-Building Controls, Sensors, Modeling, and Analytics

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Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks

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The citylearn challenge 2021

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Optimizing organic Rankine cycle systems: a hierarchical reinforcement learning approach to design and control

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Artificial Intelligence Applications in Smart Cities: Integrating Communication, Energy Management, Cybersecurity and Internet of Things

Malik, M. T., Muqeet, H. A., Akram, R., Obaid, H. M., Rehman, A. U., & Bermak, A. (2026) · The Journal of Engineering · doi:10.1049/tje2.70166 · Cited by 3 · see citing works

Energy efficient hierarchical multi-agent graph transformer architecture for decentralized demand response and energy trading in blockchain-coordinated smart grids

Kothuru, V., majeti, K., Dwivedi, S., Lingaraj, K., Lavanya, M., & Gupta, A. S. A. L. G. G. (2026) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2026.101346 · Cited by 3 · see citing works

Deep Reinforcement Learning Based HVAC Optimization Control Algorithm Application

Zeng, L., Cen, J., Liu, X., Wu, Y., & Tan, X. (2023) · IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE) · doi:10.1109/iceace60673.2023.10442090 · 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

From Simulation to Reality: A Study of Reinforcement Learning Control in Operational Building Environments

Wang, X., Mahdavi, N., Aryai, V., Sethuvenkatraman, S., & West, S. R. (2025) · ASME Journal of Engineering for Sustainable Buildings and Cities · doi:10.1115/1.4070007 · Cited by 3 · see citing works

Designing A WSNs-based Smart Home Monitoring System through Deep Reinforcement Learning

Taqwa, A., Isa, I. G. T., & Ariyanti, I. (2023) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v7i5.5143 · Cited by 3 · see citing works

Collaborative Encirclement of Multiple UAVs Based on Deep Reinforcement Learning

Jiang, W., Cai, T., Zheng, C., & Wang, Y. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10588163 · Cited by 3 · see citing works

Enhancing Grid Stability through Grid-Interactive Efficient Buildings with Deep Reinforcement Learning: Innovations and Challenges

Amer, A. A., Bayhan, S., Abu‐Rub, H., Ehsani, M., & Massoud, A. M. (2024) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon55916.2024.10905725 · Cited by 3 · see citing works

A review of reinforcement learning based approaches for industrial demand responsePreprint

Shah, M., Zhou, Y., Wu, J., & Mowbray, M. (2024) · doi:10.46855/energy-proceedings-10959 · Cited by 3 · see citing works

AI Techniques for Smart Grid Load Forecasting: An Overview With Focus on External Factors

Noori, F., & Khezrabad, A. N. (2024) · Smart Grid Conference · doi:10.1109/sgc64640.2024.10983755 · Cited by 2 · see citing works

Networked Multi-Agent Deep Reinforcement Learning Framework for the Provision of Ancillary Services in Hybrid Power Plants

Ikram, M., Habibi, D., & Aziz, A. (2025) · Energies · doi:10.3390/en18102666 · Cited by 3 · see citing works

Empowering sustainable energy systems with AI : Opportunities, challenges, and strategic solutions

Srivastava, A., Meena, P. K., Burande, C. G., Nayak, C., Wagle, C. S., & Jhalani, A. (2025) · Environmental Progress & Sustainable Energy · doi:10.1002/ep.70166 · Cited by 2 · see citing works

A review of deep reinforcement learning algorithms for grid services in grid-interactive efficient buildings

Amer, A. A., Bayhan, S., Abu‐Rub, H., Massoud, A. M., & Ehsani, M. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.12.037 · Cited by 2 · see citing works

Optimal day-ahead scheduling for explicit demand response provision of a renewable energy hub with hot water preparation

Karneluti, A., Lovrić, P., & Vašak, M. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127562 · Cited by 2 · see citing works

Optimization Strategies in Residential Energy Management Systems: A Comprehensive Review

Alsultan, A. M. T., Bani, N. A., Muhtazaruddin, M. N., Muhammad‐Sukki, F., Usman, S., Ahmad, N., Kamarudin, M. K. A., & Zulkipli, M. (2026) · International Journal of Energy Research · doi:10.1155/er/6685065 · Cited by 2 · see citing works

Achieving SDGs Using AI Techniques and Digital Twins for Nuclear Power Plants: A Review

Barik, K., Misra, S., & Thunem, H. P.-J. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-53433-1_5 · Cited by 2 · see citing works

Demand Response in Building Microgrids with Variational Quantum Circuit Enabled Hybrid Control Strategy

Ajagekar, A., & You, F. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.480 · Cited by 2 · see citing works

Hierarchical control strategy for heating systems using multi-agent deep reinforcement learning

Wu, Y., Cong, M., Lu, Q., Zhou, Z., Miao, Y., Liu, J., & Yang, D. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112699 · Cited by 2 · see citing works

A Custom Reinforcement Learning Environment for Hybrid Renewable Energy Systems: Design and Implementation

Filho, D. F. G., Moret, M. A., & Nascimento, E. G. S. (2025) · IEEE Access · doi:10.1109/access.2025.3593064 · Cited by 2 · see citing works

Improved typical scenario-based distributionally robust co-dispatch of energy and reserve for renewable power systems considering the demand response of fused magnesium load

Qian, J., Cai, J., Hao, L., & Meng, Z. (2024) · Frontiers in Energy Research · doi:10.3389/fenrg.2024.1401080 · Cited by 2 · see citing works

Actor-Critic Decision-Making in Wsns-Based Water Reservoir Monitoring SystemPreprint

Sung, W., Isa, I. G. T., & Hsiao, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4868688 · Cited by 2 · see citing works

SAC-Based Distributed Optimal Control of Generation Cost in DC Microgrids

Khanghah, M. S., Moghaddam, F., Suratgar, A. A., & Menhaj, M. B. (2025) · IEEE International Conference on DC Microgrids · doi:10.1109/icdcm63994.2025.11144645 · Cited by 1 · see citing works

Optimal Carbon Emission Reduction Modeling Considering Energy Consumer Satisfaction in Cyber-Physical Energy System

Wang, C., Guan, X., Wang, N., Chen, H., Ohtsuki, T. O., & Han, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3637169 · Cited by 1 · see citing works

Deep Reinforcement Learning for Scheduling of a Steel Plant in the Electricity Spot Market

Shah, M., Zhou, Y., Wu, J., & Mowbray, M. (2026) · Engineering · doi:10.1016/j.eng.2025.12.038 · Cited by 1 · see citing works

Artificial intelligence in electric cars connected to grid

Madichetty, S., Koduru, S. S., & Pathan, M. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-95045-9.00012-3 · Cited by 1 · see citing works

Assessing individual and centralized demand response in renewable energy-rich areas for microgrids

Zheng, X., Bai, F., & Jin, T. (2026) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2026.102224 · Cited by 1 · see citing works

A Control-Centric Systematic Review of MARL for EV–Grid Coordination: From Predictive Input to Verifiable Feedback

Nazloo, H. T., & Musı́lek, P. (2026) · Electronics · doi:10.3390/electronics15091902 · Cited by 1 · see citing works

A Review Informed Translation Framework for Mapping Smart Building Services into Smart Readiness Indicator Aligned Assessment

Jôrgensen, B. N., Staugaard, B. E., Madsen, S. S., & Ma, Z. G. (2026) · Buildings · doi:10.3390/buildings16101998 · Cited by 2 · see citing works

Decarbonizing livestock villages: Synergistic electricity-heat-fertilizer systems with demand response

Wu, B., Wang, X., Wang, X., Shan, B., Shao, C., Yuan, J., & Ji, S. (2026) · Energy · doi:10.1016/j.energy.2026.141468 · Cited by 1 · see citing works

Artificial intelligence technologies for electric vehicle charging and hydrogen refueling systems: A transition of sustainable energy transportation

Kavousighahfarokhi, A., Hannan, M. A., Ker, P. J., Roslan, M. F., Hossain, M. J., & Jang, G. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.155754 · Cited by 1 · see citing works

Scalable physics-informed multi-agent reinforcement learning for building energy system control

Wang, X., Ren, Z., Li, N., & Dong, B. (2026) · Advances in Applied Energy · doi:10.1016/j.adapen.2026.100285 · Cited by 1 · see citing works

Toward sustainable PV–EV integrated smart homes: A critical review and data-driven reinforcement learning–enabled cyber–physical framework

Ranjbar, M. A., Mohammadi, B., Baghaee, H. R., & Haghifam, M. R. (2026) · Energy Strategy Reviews · doi:10.1016/j.esr.2026.102303 · Cited by 1 · 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

氢电耦合储能系统：模型、应用和深度强化学习算法

郑杰辉, 苏盈盈, 王文浩, 李志刚, & 吴青华 (2024) · Clean Energy Science and Technology · doi:10.18686/cncest.v2i1.146

Multi-agent Deep Reinforcement Learning for Energy Management in Grid-Responsive Networked Greenhouses

Decardi‐Nelson, B., Ajagekar, A., & You, F. (2024) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc60939.2024.10644977

Energy Scheduling and Control of Grid-Interactive Communities with Physically Consistent Deep Learning

Xiao, T., & You, F. (2024) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc60939.2024.10644541

Entropy-regularized neural learning for dynamic and weighted consensus control of storage units in low-voltage distribution network

Li, S., Wang, Y., & Vu, M. T. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101886

Research on central air conditioning demand response optimization based on DDPG

Sun, Z. D., & Deng, Z. (2025) · International Conference on Next Generation Data-driven Networks (NGDN) · doi:10.1109/ngdn66208.2025.11182122

Implementing Aggregated Demand Response in Malaysia’s Commercial Sector

Hussain, I. S., Yussof, S., Mansor, M., & Jôrgensen, B. N. (2025) · Lecture notes in civil engineering · doi:10.1007/978-981-96-7818-1_41

Energy cost and emission optimization in food industry using deep reinforcement learning and Transformer-based load forecasting

Wohlgenannt, P., Moosbrugger, L., Vetter, V., Hegenbart, S., Kolhe, M. L., Eder, E., & Kepplinger, P. (2025) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2025.101364

AMPERE: A Hierarchical Multi-Agent Deep Reinforcement Learning and Evolutionary Algorithm Framework for Optimising Smart Grid Electricity Distribution

Diepeveen, A., & Sithungu, S. P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13203-1_15

Pre-optimization-based deep reinforcement learning for autonomous economic and exergy management of stochastic H2/CO ratio syngas switching downstream processes

Sun, Y., Liu, Z., Cui, Z., Wang, W., & Tian, W. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.147560

Research and Practice on Demand Response of Smart Grid Regulation Technology

Su, H., Wang, S., Liu, H., Guo, P., & Liu, X. (2025) · IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · doi:10.1109/aeeca65693.2025.00011

Grid Automation Control using Reinforcement Learning

Li, B. F., Lei, Y., Dou, M., Chang, X., Wang, L., Qiu, Z., & Wang, L. (2025) · International Conference on Mobile Networks and Wireless Communications (ICMNWC) · doi:10.1109/icmnwc66779.2025.11354360

Unsupervised learning-enhanced neighborhood relationship utilization for intrinsic reward-driven multi-agent exploration

Wang, H., Wang, Z., Yang, L., & Shi, H. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115540

Application of AI in the Load Forecasting for Smart Grid

Mishra, V. K., Verma, H. K., & Parikh, A. (2025) · International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) · doi:10.1109/ietacs68750.2025.11385399

ConsenNet–based fully decentralized MARL for residential flexibility coordination in resilient community microgrids

Huang, P., Ma, C., Xu, M., Hua, H., Zhao, J., & Lei, S. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117583

AttentionKAN-Based Multi-Agent Reinforcement Learning for Coordinated Battery Energy Storage Control in Residential Demand Response

Sajid, S., Li, B., Qi, B., Berehman, B., Liang, F., Lei, Y., & Muqtadir, A. (2026) · Sustainability · doi:10.3390/su18094536

Green AI: A Study of Optimization Approaches, Sustainability Metrics, Applications and Emerging Challenges

Kadhim, H. K. A. (2026) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v5i4.1399

Research on Pricing Strategies for Demand Response Resources Participating in the Spot Market

Zhao, Q., Pan, T., Zhou, Q., Tian, S., & Zhao, J. (2026) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee69242.2026.11524315

Optimal Operation of Virtual Power Plants Considering Internal Carbon Cycle: Based on CCS-P2G Coupled System

Pan, T., Zhao, Q., Tian, S., Zhou, Q., & Zhao, J. (2026) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee69242.2026.11524953

Attention Based Multi-Agent Reinforcement Learning for Demand Response in Grid-Responsive Buildings

Xie, J., Ajagekar, A., & You, F. (2023) · IEEE Conference on Control Technology and Applications (CCTA) · doi:10.1109/ccta54093.2023.10253019 · Cited by 5 · see citing works

Demand Response in Microgrids with Attention-Based Deep Reinforcement Learning

Xie, J., Ajagekar, A., & You, F. (2023) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-443-15274-0.50249-3

Enhancing Grid-Interactive Buildings Demand Response: Sequential Update-Based Multiagent Deep Reinforcement Learning Approach

Han, Y., Wu, J., Chen, H., Si, F., Cao, Z., & Zhao, Q. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3357109 · Cited by 26 · see citing works

Optimizing grid-interactive buildings demand response: Sequence-based decision-making multi-agent policy decomposition deep reinforcement learning

Han, Y., Gao, W., Wang, Z., & Zhao, Q. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116198 · Cited by 8 · see citing works

Demand Response through Price-setting Multi-agent Reinforcement Learning

Christensen, M. H., Ernewein, C., & Pinson, P. (2020) · doi:10.1145/3427773.3427862 · Cited by 10 · see citing works