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Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach

Author

Listed:
  • Huichao Ji

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
    School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Junyou Yang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Haixin Wang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Kun Tian

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Martin Onyeka Okoye

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jiawei Feng

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.

Suggested Citation

  • Huichao Ji & Junyou Yang & Haixin Wang & Kun Tian & Martin Onyeka Okoye & Jiawei Feng, 2019. "Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach," Energies, MDPI, vol. 12(24), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4744-:d:297254
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    References listed on IDEAS

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    1. Nigitz, Thomas & Gölles, Markus, 2019. "A generally applicable, simple and adaptive forecasting method for the short-term heat load of consumers," Applied Energy, Elsevier, vol. 241(C), pages 73-81.
    2. Guizhi Xu & Xiao Hu & Zhirong Liao & Chao Xu & Cenyu Yang & Zhanfeng Deng, 2018. "Experimental and Numerical Study of an Electrical Thermal Storage Device for Space Heating," Energies, MDPI, vol. 11(9), pages 1-14, August.
    3. Attonaty, Kevin & Stouffs, Pascal & Pouvreau, Jérôme & Oriol, Jean & Deydier, Alexandre, 2019. "Thermodynamic analysis of a 200 MWh electricity storage system based on high temperature thermal energy storage," Energy, Elsevier, vol. 172(C), pages 1132-1143.
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    Cited by:

    1. Ji, Huichao & Wang, Haixin & Yang, Junyou & Feng, Jiawei & Yang, Yongyue & Okoye, Martin Onyeka, 2021. "Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks," Energy, Elsevier, vol. 234(C).
    2. Xiongchao Lin & Wenshuai Xi & Jinze Dai & Caihong Wang & Yonggang Wang, 2020. "Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes," Energies, MDPI, vol. 13(19), pages 1-18, October.
    3. Pedro Faria & Zita Vale, 2023. "Demand Response in Smart Grids," Energies, MDPI, vol. 16(2), pages 1-3, January.

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