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Operation scheduling in a solar thermal system: A reinforcement learning-based framework

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  • Correa-Jullian, Camila
  • López Droguett, Enrique
  • Cardemil, José Miguel

Abstract

Reinforcement learning (RL) provides an alternative method for designing condition-based decision making in engineering systems. In this study, a simple and flexible RL tabular Q-learning framework is employed to identify the optimal operation schedules for a solar hot water system according to action–reward feedback. The system is simulated in TRNSYS software. Three energy sources must supply a building’s hot-water demand: low-cost heat from solar thermal collectors and a heat-recovery chiller, coupled to a conventional heat pump. Key performance indicators are used as rewards for balancing the system’s performance with regard to energy efficiency, heat-load delivery, and operational costs. A sensitivity analysis is performed for different reward functions and meteorological conditions. Optimal schedules are obtained for selected scenarios in January, April, July, and October, according to the dynamic conditions of the system. The results indicate that when solar radiation is widely available (October through April), the nominal operation schedule frequently yields the highest performance. However, the obtained schedule differs when the solar radiation is reduced, for instance, in July. On average, with prioritization of the efficient use of both low-cost energy sources, the performance in July can be on average 21% higher than under nominal schedule-based operation.

Suggested Citation

  • Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920304554
    DOI: 10.1016/j.apenergy.2020.114943
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    Cited by:

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    3. Lillo-Bravo, I. & Vera-Medina, J. & Fernandez-Peruchena, C. & Perez-Aparicio, E. & Lopez-Alvarez, J.A. & Delgado-Sanchez, J.M., 2023. "Random Forest model to predict solar water heating system performance," Renewable Energy, Elsevier, vol. 216(C).
    4. Heidari, Amirreza & Maréchal, François & Khovalyg, Dolaana, 2022. "An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 312(C).
    5. Zhou, Xin & Tian, Shuai & An, Jingjing & Yan, Da & Zhang, Lun & Yang, Junyan, 2022. "Modeling occupant behavior’s influence on the energy efficiency of solar domestic hot water systems," Applied Energy, Elsevier, vol. 309(C).
    6. Zedong Jiao & Xiuli Du & Zhansheng Liu & Liang Liu & Zhe Sun & Guoliang Shi & Ruirui Liu, 2023. "A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    7. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    8. Zhang, Xiongfeng & Lu, Renzhi & Jiang, Junhui & Hong, Seung Ho & Song, Won Seok, 2021. "Testbed implementation of reinforcement learning-based demand response energy management system," Applied Energy, Elsevier, vol. 297(C).
    9. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    10. 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).

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