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Group Consensus-Driven Energy Consumption Assessment Using Social Network Analysis and Fuzzy Information Fusion

Author

Listed:
  • Bingjie Wang

    (Shanxi University, China)

  • Chao Zhang

    (Shanxi University, China)

  • Arun Kumar Sangaiah

    (National Yunlin University of Science and Technology, Taiwan & Sunway University, Malaysia)

  • Mohammed J. F. Alenazi

    (King Saud University, Saudi Arabia)

  • Salman A. AlQahtani

    (King Saud University, Saudi Arabia)

  • Sendhil Kumar K. S.

    (VIT University, India)

Abstract

With industrial development deepening, the share of industrial energy in overall consumption has notably risen. To assess industrial energy consumption accurately, this paper proposes a method employing interval type-2 fuzzy sets (IT2FSs) to represent assessment information effectively. Additionally, it analyzes decision-makers (DMs) as a social network to alleviate individual biases. IT2FSs are chosen to handle uncertainties in assessing industrial energy consumption. Addressing biases in DMs' opinions, a group consensus model aids the consensus reaching process (CRP). Industrial energy consumption is assessed using the MULTIMOORA method, yielding three results. These are fused via D-S evidence theory (DSET) to obtain the final assessment. Finally, the model's effectiveness is verified with a case study on energy consumption in the steel industry. In conclusion, this paper not only deepens the understanding of uncertainties in the energy consumption assessment process, but also provides a robust tool for various industries to optimize energy use and economic outcomes.

Suggested Citation

  • Bingjie Wang & Chao Zhang & Arun Kumar Sangaiah & Mohammed J. F. Alenazi & Salman A. AlQahtani & Sendhil Kumar K. S., 2024. "Group Consensus-Driven Energy Consumption Assessment Using Social Network Analysis and Fuzzy Information Fusion," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-32, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-32
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