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.
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