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Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application

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  • Cai, Qingsen
  • Luo, XingQi
  • Wang, Peng
  • Gao, Chunyang
  • Zhao, Peiyu

Abstract

Energy forms the foundation for humans, but with the wastage of energy and energy consumption being at critical levels, energy saving has become an urgent priority. As a general term for various complex energy systems, energy hub and its control have become key research objects with regard to energy saving. In terms of control of an energy hub, the traditional model-driven approach and the currently rapidly developing data-driven approach have their own characteristics. In this paper, we propose a hybrid model-driven method and a data-driven control method using machine learning algorithms to combine the characteristics of the two driven approaches to realize the extraction of the data hiding mode. The Koopman operation is used to increase the dimension of the data to be linearized. Subsequently, the singular value decomposition method decomposes the data in polar coordinates, reducing dimensionality while ensuring linearization. The polynomial model obtained through machine learning training is simple and flexible. The online data of the energy hub can be used for fast coefficient fitting, and the speed and accuracy of the model can be guaranteed when external conditions change. Case studies on a circulating cooling water system show that this method could complete the process of data collection, learning, modeling, and control automatically when external changes occurred, and the time required for the entire process could meet the needs of a control response. It could rapidly and accurately complete the control process as well as effectively reduce the energy consumption, and it did not generate excessive control costs in the process.

Suggested Citation

  • Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012253
    DOI: 10.1016/j.apenergy.2021.117913
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    Cited by:

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    2. 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).
    3. Jacek Czyżewicz & Piotr Jaskólski & Paweł Ziemiański & Marian Piwowarski & Mateusz Bortkiewicz & Krzysztof Laszuk & Ireneusz Galara & Marta Pawłowska & Karol Cybulski, 2022. "Towards Designing an Innovative Industrial Fan: Developing Regression and Neural Models Based on Remote Mass Measurements," Energies, MDPI, vol. 15(7), pages 1-19, March.
    4. Mu, Yunfei & Xu, Yurui & Cao, Yan & Chen, Wanqing & Jia, Hongjie & Yu, Xiaodan & Jin, Xiaolong, 2022. "A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model," Applied Energy, Elsevier, vol. 323(C).
    5. Ding, Yakui & Li, Yongping & Zheng, Heran & Meng, Jing & Lv, Jing & Huang, Guohe, 2022. "Identifying critical energy-water paths and clusters within the urban agglomeration using machine learning algorithm," Energy, Elsevier, vol. 250(C).
    6. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

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