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A two-level hierarchical optimization framework for grid-connected photovoltaic-wind-battery systems in greenhouse energy management

Ren, Z., Xu, M., Zhao, Z., Wang, X., Guo, J., & Dong, Y. (2026) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2026.102181 · Cited by 2 · see citing works

Hybrid sorption thermal–battery storage in photovoltaic greenhouses: Toward net-zero energy and reduced battery stress

Gholami, M., Li, D., Lin, S., & Muyeen, S. M. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.122254 · 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

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Lachheb, A., Skouri, S., Ayed, R., Bekkouche, S. M. E. A., & Bouadila, S. (2024) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2024.2329814 · Cited by 2 · see citing works

Assessment of AI-Based Robust Model Predictive Control Application in Large-Scale Photovoltaic-Based Controlled Environment Agriculture for Urban Agriculture

Hu, G., & You, F. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.510 · 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

Placement and sizing of photovoltaic and bio-waste unit with hydrogen storage considering economic energy management of intelligent distribution network

Younes, R., Gafsi, N., Chyad, M. H., Sharma, K., Hussein, S. A., Singh, N. S. S., Hamdouni, A., & Akbari-Zadeh, M.-R. (2025) · Scientific Reports · doi:10.1038/s41598-025-22065-4 · Cited by 1 · see citing works

Offering reserve capacity to renewable-rich power systems can cut plant factory energy costs by up to 87 %

Zhang, Y., & You, F. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127145 · Cited by 1 · see citing works

Toward resource-intelligent greenhouses: A prospective vision for drylands

Thakur, A. K., Ferguson, A., Ahamed, M. S., Miao, Z., Attia, M., Hung, T.-C., Fath, H., Kassem, A. S., Jailany, A. T., & Eldin, A. Z. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107035 · Cited by 1 · see citing works

Residential Building HVAC Load Energy Management using Reinforced Learning and Soft Actor-Critic Algorithm

Babu, P. S. K., Marimuthu, P., Baskaran, S., & Kumar, R. (2025) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon67893.2025.11327218 · Cited by 1 · see citing works

Quantum-enhanced forecasting and optimal scheduling for energy management in photovoltaic greenhouses

Weng, Z., Xu, Z., Li, R., Wang, L., Li, Z., Zhang, Y., & Li, M. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128142 · Cited by 1 · see citing works

Significant boost in energy efficiency of high-tech greenhouse capsicum amid projected climate scenarios

He, J., Liang, C., Wang, Z., Lin, T. C., Hou, B., Liang, W., Feng, X., Metternicht, G. I., Lan, Y.-C., Tissue, D. T., et al. (2026) · Energy Nexus · doi:10.1016/j.nexus.2026.100700 · Cited by 1 · see citing works

Agentic Artificial Intelligence in Agriculture: A Systematic Mapping Review of Reported Architectures, Applications, Challenges, and Future Directions

Meghraouı, K., & Moussaid, A. (2026) · Technologies · doi:10.3390/technologies14090591 · Cited by 1 · see citing works

Considering the Carbon Trading Mechanism for Collaborative Optimization of Source and Load Operations in High Energy Consuming Enterprises

Xu, B., Huang, Y., & Peng, D. (2024) · International Conference on Electrical Engineering and Control Technologies (CEECT) · doi:10.1109/ceect63656.2024.10898958 · Cited by 1 · see citing works

Coordinated Optimization of Tourist-based Rural Communities Integrating Tourist Electric Buses and Greenhouses

Liu, Y., Xu, X. S., Liu, J., Hu, W., Yang, N., Jawad, S., & Wei, Z. (2024) · Asia Energy and Electrical Engineering Symposium (AEEES) · doi:10.1109/aeees61147.2024.10544997

Incentive Mechanism for SCDL-Based Demand Response

Yunfei, S., Fan, S., Wang, X., Jia, K., Yixuan, H., & He, G. (2024) · IEEE Sustainable Power and Energy Conference (iSPEC) · doi:10.1109/ispec59716.2024.10892475

Evaluation of User Adjustable Capacity Boundary Based on FCM Clustering and Multi-Factor Electricity Price Elasticity

Yang, W., Zhu, T., Cheng, M., & Xia, W. (2024) · International Conference on Electrical Engineering and Control Science (IC2ECS) · doi:10.1109/ic2ecs64405.2024.10928682

Multi-Agent Deep Reinforcement Learning for Resource Management in On-Demand Environments

Chahoud, M., Sami, H., Mizouni, R., Otrok, H., Bentahar, J., Mourad, A., & Talhi, C. (2025) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc65282.2025.11059489

Smart Grid Enabled Indoor Farming: A New Recipe for Energy Management Using Lighting Control

Abbaspour, M., Shukla, M. R., Saxena, P. K., & Saxena, S. (2025) · Global Power, Energy and Communication Conference · doi:10.1109/gpecom65896.2025.11061887

Smart Greenhouse Climate Monitoring and Control

Сацик, В. О., Reshetylo, O., Markina, L., Грудецький, Р. Я., Smoliankin, O., & Lapchenko, Y. (2024) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert65323.2024.11122249

Towards an Energy-Efficient Urban Industrial-Scale Aquaponics Integrated with PV-Battery System: An Aggregator-Based Demand Response ApproachPreprint

Zheng, Y., Liu, S., You, F., Chen, Y., & Li, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5538711

Evaluation of load-side flexibility supply capability considering source-load interaction in power system operation

Li, J., Luo, L., Chen, J., Wei, S., & Li, B. (2026) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2026.102132

Geothermal-Based and Grid-Connected Energy Optimization of Smart Greenhouse Using Energy Valley Optimizer: A Case Study in Regina, Canada

Ghiasi, M. I., Wang, Z., & Mehrandezh, M. (2025) · IEEE International Conference on Energy Technologies for Future Grids (ETFG) · doi:10.1109/etfg61999.2025.11401213

Multi-Agent Deep Reinforcement Learning Based Adaptive Control for Smart Greenhouse Integrated Microgrid

Tran, T. M., Ouammi, A., & Dessaint, L. (2025) · International Congress on Smart Agriculture and Sustainable Systems (SmartAgri&SuSY) · doi:10.1109/smartagrisusy68475.2025.11466820

Optimize electricity considering installation costs and carbon emissions: A comparison between reinforcement learning and linear programming

Wang, C.-H., Ting, M.-J., & Tseng, S.-C. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109308

A Multi-Agent Reinforcement Learning Strategy for Dehumidification and Temperature Control in Intelligent Safety Tool Cabinets

Li, G., Weng, J., Feng, K., & Yao, F. (2025) · International Conference on Smart Energy and Control Engineering (SECE) · doi:10.1109/sece67562.2025.00010

Behavior cloning-guided multi-agent deep reinforcement learning for greenhouse-microgrid control

Tran, T. M., Ouammi, A., & Dessaint, L. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102275

Advancing food-energy-water sustainability with scalable perovskite tandem agrivoltaics

Tian, X., & You, F. (2026) · Nexus · doi:10.1016/j.ynexs.2026.100151

Stochastic Scheduling of Integrated System of Solar Resources and Hydrogen Storage in the Smart Distribution Network Considering a Multi-Objective Energy Management Model

Akbari, E. (2025) · Journal of Green Energy Research and Innovation · doi:10.66224/jgeri.2.3.44

Multi-agent deep reinforcement learning for unmanned aerial vehicle navigation: A comprehensive survey

Alzboun, B. M., Abed-alguni, B. H., & Qtaish, O. K. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134600

Online neural fusion of deep reinforcement learning and model predictive control for comfort-aware greenhouse energy optimization

Lu, C., & You, F. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128621

Day-Ahead Bidding for an Aggregator of Crop-Aware Greenhouses

Zhao, J., Wang, G., Liu, J., Dai, Y., Lu, Y., Chen, S., & Zhang, Z. (2026) · Energies · doi:10.3390/en19163796

Price-responsive energy, water, and carbon cost assessment of greenhouse climate control under South African time-of-use tariffs

Ren, Z., Guo, J., Hu, M., Lin, N., Feng, Y., & Dong, Y. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105320

Intelligent Building Energy Control Systems: A Review of Multi-Agent Reinforcement Learning Technologies, Challenges, and Future Directions

Kapparova, A., Galland, S., Lombard, A., Nurgaliyev, M., Dosymbetova, G., Kaniyev, Y., Saymbetov, A., Bolatbek, A., Orynbassar, S., & Жоламанов, Б. (2026) · Smart Cities · doi:10.3390/smartcities9100177

Giving the greenhouse a brain: How artificial intelligence is transforming controlled environment agriculture

Fuentes, A., Yoon, S., Park, D. S., & Gruda, N. S. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112608

Coordinated Planning and Scheduling of Rural Integrated-Energy Systems Considering Crop-Growth Requirements and Agricultural-Load Flexibility

Pan, Z., Hu, J., Su, Y., Zhang, M., Zhou, N., & Zhao, J. (2026) · Processes · doi:10.3390/pr14213531

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

Hierarchical Distributed Energy Management Framework for Multiple Greenhouses Considering Demand Response

Rezaei, E., Dagdougui, H., & Ojand, K. (2022) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2022.3215686 · Cited by 22 · see citing works

Behavior cloning-guided multi-agent deep reinforcement learning for greenhouse-microgrid control

Tran, T. M., Ouammi, A., & Dessaint, L. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102275

Multi-Agent Deep Reinforcement Learning Based Adaptive Control for Smart Greenhouse Integrated Microgrid

Tran, T. M., Ouammi, A., & Dessaint, L. (2025) · International Congress on Smart Agriculture and Sustainable Systems (SmartAgri&SuSY) · doi:10.1109/smartagrisusy68475.2025.11466820

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

Xie, J., Ajagekar, A., & You, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121162 · Cited by 109 · see citing works