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Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system

Author

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  • Li, Hongcheng
  • Yang, Dan
  • Cao, Huajun
  • Ge, Weiwei
  • Chen, Erheng
  • Wen, Xuanhao
  • Li, Chongbo

Abstract

Advances in energy-saving technology is main way to achieve carbon neutrality. With the development of digital twin, building the physical-virtual data space for improving energy management capacity of enterprises has received tremendous attention. The energy behaviour model implementing accurate simulation and prediction of energy state is the core meta-model of energy-efficient manufacturing digital twin (EMDT). The widely used state-based energy modelling assumes constant power in operation state and approximately fits the energy behaviour without considering uncertain operation environment, resulting in energy behaviour distortion. A data-driven hybrid petri-net (DDHPN) inspired by both the state-based energy modelling and machine learning was developed for establishing the energy behaviour meta-model. Gaussian kernel extreme learning machine is proposed to fit the instantaneous firing speed of energy consumption continuous transitions in DDHPN. DDHPN-based energy behaviour model is driven by physical data under real-time working conditions, operating parameters, and production load for generating a virtual data space of energy management. Finally, DDHPN was integrated into the EMDT model using unified modelling language. The application in extrusion process and die casting process show that the presented model has higher accuracy in energy behaviour prediction. Furthermore, a digital-twin-based energy management prototype system for extrusion workshop demonstrates its potential.

Suggested Citation

  • Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024269
    DOI: 10.1016/j.energy.2021.122178
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    References listed on IDEAS

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    Cited by:

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    2. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    3. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Dassisti, Michele & Olabi, A.G., 2023. "Guidelines for designing a digital twin for Li-ion battery: A reference methodology," Energy, Elsevier, vol. 284(C).
    4. Frafjord, Aksel Johan & Radicke, Jan-Philip & Keprate, Arvind & Komulainen, Tiina M., 2024. "Data-driven approaches for deriving a soft sensor in a district heating network," Energy, Elsevier, vol. 292(C).
    5. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).

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