IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v229y2021ics0360544221009737.html
   My bibliography  Save this item

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

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Razzano, Giuseppe & Brandi, Silvio & Piscitelli, Marco Savino & Capozzoli, Alfonso, 2025. "Rule extraction from deep reinforcement learning controller and comparative analysis with ASHRAE control sequences for the optimal management of Heating, Ventilation, and Air Conditioning (HVAC) syste," Applied Energy, Elsevier, vol. 381(C).
  2. Tungom, Chia E. & Wang, Hong & Beata, Kamuya & Niu, Ben, 2024. "SWOAM: Swarm optimized agents for energy management in grid-interactive connected buildings," Energy, Elsevier, vol. 301(C).
  3. Schmitz, Simon & Brucke, Karoline & Kasturi, Pranay & Ansari, Esmail & Klement, Peter, 2024. "Forecast-based and data-driven reinforcement learning for residential heat pump operation," Applied Energy, Elsevier, vol. 371(C).
  4. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
  5. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
  6. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
  7. Hampton, Harrison & Foley, Aoife, 2022. "A review of current analytical methods, modelling tools and development frameworks applicable for future retail electricity market design," Energy, Elsevier, vol. 260(C).
  8. Amr A. Elshazly & Mahmoud M. Badr & Mohamed Mahmoud & William Eberle & Maazen Alsabaan & Mohamed I. Ibrahem, 2024. "Reinforcement Learning for Fair and Efficient Charging Coordination for Smart Grid," Energies, MDPI, vol. 17(18), pages 1-28, September.
  9. Amr A. Elshazly & Islam Elgarhy & Mohamed Mahmoud & Mohamed I. Ibrahem & Maazen Alsabaan, 2025. "A Privacy-Preserving RL-Based Secure Charging Coordinator Using Efficient FL for Smart Grid Home Batteries," Energies, MDPI, vol. 18(4), pages 1-34, February.
  10. Soleimanzade, Mohammad Amin & Kumar, Amit & Sadrzadeh, Mohtada, 2022. "Novel data-driven energy management of a hybrid photovoltaic-reverse osmosis desalination system using deep reinforcement learning," Applied Energy, Elsevier, vol. 317(C).
  11. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  12. Muqing Wu & Qingsu He & Yuping Liu & Ziqiang Zhang & Zhongwen Shi & Yifan He, 2022. "Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
  13. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
  14. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).
  15. Pinto, Giuseppe & Kathirgamanathan, Anjukan & Mangina, Eleni & Finn, Donal P. & Capozzoli, Alfonso, 2022. "Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures," Applied Energy, Elsevier, vol. 310(C).
  16. Chen, Minghao & Sun, Yi & Xie, Zhiyuan & Lin, Nvgui & Wu, Peng, 2023. "An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning," Energy, Elsevier, vol. 284(C).
  17. Hashemipour, Naser & Crespo del Granado, Pedro & Aghaei, Jamshid, 2021. "Dynamic allocation of peer-to-peer clusters in virtual local electricity markets: A marketplace for EV flexibility," Energy, Elsevier, vol. 236(C).
  18. Eduardo J. Salazar & Mauro Jurado & Mauricio E. Samper, 2023. "Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
  19. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
  20. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
  21. Panagiotis Michailidis & Iakovos Michailidis & Elias Kosmatopoulos, 2025. "Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations," Energies, MDPI, vol. 18(7), pages 1-40, March.
  22. Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).
  23. Charalampos Rafail Lazaridis & Iakovos Michailidis & Georgios Karatzinis & Panagiotis Michailidis & Elias Kosmatopoulos, 2024. "Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management," Energies, MDPI, vol. 17(3), pages 1-33, January.
  24. Nweye, Kingsley & Sankaranarayanan, Siva & Nagy, Zoltan, 2023. "MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities," Applied Energy, Elsevier, vol. 346(C).
  25. Dimitrios Vamvakas & Panagiotis Michailidis & Christos Korkas & Elias Kosmatopoulos, 2023. "Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications," Energies, MDPI, vol. 16(14), pages 1-38, July.
  26. Silvestri, Alberto & Coraci, Davide & Brandi, Silvio & Capozzoli, Alfonso & Borkowski, Esther & Köhler, Johannes & Wu, Duan & Zeilinger, Melanie N. & Schlueter, Arno, 2024. "Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control," Applied Energy, Elsevier, vol. 368(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.