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MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities

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  • Nweye, Kingsley
  • Sankaranarayanan, Siva
  • Nagy, Zoltan

Abstract

Building and power generation decarbonization present new challenges in electric grid reliability as a result of renewable energy source intermittency and increase in grid load caused by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide grid flexibility services through demand response. Reinforcement learning is well-suited for energy management in grid-interactive efficient buildings as it is able to adapt to unique building characteristics compared to rule-based control and model predictive control. Yet, factors hindering the adoption of reinforcement learning in real-world applications include its sample inefficiency during training, control security and generalizability. Here we address these challenges by proposing the MERLIN framework for the training, evaluation, deployment and transfer of control policies for distributed energy resources in grid-interactive communities for different levels of data availability. We utilize a real-world community smart meter dataset to show that while independently trained battery control policies can learn unique occupant behavior and provide up to 60% performance improvement at the district level, transfer learning provides comparable building and district level performance while reducing training costs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:346:y:2023:i:c:s0306261923006876
    DOI: 10.1016/j.apenergy.2023.121323
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    1. Burger, Scott & Chaves-Ávila, Jose Pablo & Batlle, Carlos & Pérez-Arriaga, Ignacio J., 2017. "A review of the value of aggregators in electricity systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 395-405.
    2. Oriol Vinyals & Igor Babuschkin & Wojciech M. Czarnecki & Michaël Mathieu & Andrew Dudzik & Junyoung Chung & David H. Choi & Richard Powell & Timo Ewalds & Petko Georgiev & Junhyuk Oh & Dan Horgan & M, 2019. "Grandmaster level in StarCraft II using multi-agent reinforcement learning," Nature, Nature, vol. 575(7782), pages 350-354, November.
    3. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    4. 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).
    5. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
    6. 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.
    7. Gautam Gowrisankaran & Stanley S. Reynolds & Mario Samano, 2016. "Intermittency and the Value of Renewable Energy," Journal of Political Economy, University of Chicago Press, vol. 124(4), pages 1187-1234.
    8. Leibowicz, Benjamin D. & Lanham, Christopher M. & Brozynski, Max T. & Vázquez-Canteli, José R. & Castejón, Nicolás Castillo & Nagy, Zoltan, 2018. "Optimal decarbonization pathways for urban residential building energy services," Applied Energy, Elsevier, vol. 230(C), pages 1311-1325.
    9. Peter R. Wurman & Samuel Barrett & Kenta Kawamoto & James MacGlashan & Kaushik Subramanian & Thomas J. Walsh & Roberto Capobianco & Alisa Devlic & Franziska Eckert & Florian Fuchs & Leilani Gilpin & P, 2022. "Outracing champion Gran Turismo drivers with deep reinforcement learning," Nature, Nature, vol. 602(7896), pages 223-228, February.
    10. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    11. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(C).
    12. 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).
    13. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    14. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).
    15. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    16. 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).
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    1. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).

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