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

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

Bingjie Wang, Chao Zhang, Arun Kumar Sangaiah, Mohammed J. F. Alenazi, Salman A. AlQahtani, Sendhil Kumar K. S.
Copyright: © 2024 |Pages: 33
DOI: 10.4018/IJSWIS.352043
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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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The Problem Background

Industrial energy consumption is a cornerstone of modern economies, powering manufacturing processes, transportation systems, and myriad other activities vital for societal functioning. However, the sheer scale of energy usage in industrial sectors raises significant environmental, economic, and social concerns (Zhao & Zhu, 2022). Hence, assessing industrial energy consumption becomes imperative for ensuring energy security, mitigating environmental impact, and fostering sustainable growth. Assessing industrial energy consumption (Sangaiah et al., 2023) offers multifaceted benefits with tangible real-world implications. Additionally, it facilitates informed energy consumption assessment (Gupta & Panigrahi, 2022) by providing insights into energy usage patterns, allowing policymakers, businesses, and stakeholders to identify areas of inefficiency and prioritize resource allocation. Moreover, it enables the formulation and implementation of targeted energy efficiency measures, thereby enhancing productivity, reducing operational costs, and bolstering competitiveness in global markets. Furthermore, it aids environmental sustainability endeavors by pinpointing opportunities to decrease carbon emissions, mitigate climate change, and transition to cleaner energy sources (Rao et al., 2023). Additionally, it fosters innovation and technological advancements by incentivizing research and development in energy-efficient technologies and processes.

The steel industry (Alrumaih & Alenazi, 2024) stands out as a prominent consumer of energy, accounting for a significant portion of industrial energy consumption worldwide. The production of steel involves energy-intensive processes such as iron ore smelting, coke production, and steelmaking, which collectively contribute to substantial energy usage and carbon emissions. Consequently, the steel industry faces unique challenges in terms of energy efficiency, resource optimization, and environmental sustainability. Despite these challenges, the steel industry also presents significant opportunities for energy conservation and emission reduction. By assessing its energy usage, we can identify opportunities to reduce greenhouse gas emissions, mitigate climate change, and improve air quality (Patil & Singh, 2024). Overall, assessing energy consumption (Bai et al., 2020) in the steel industry is crucial for achieving environmental, economic, and social sustainability goals. Therefore, this paper will conduct decision assessment within the context of energy consumption in the steel industry. The schematic diagram of industrial energy consumption assessment is shown in Figure 1. From Figure 1, it can be seen that in this energy consumption assessment, we use time points as decision makers and dates as alternatives. The energy consumption assessment is then conducted based on the evaluation attributes. In this paper, three classic theories are utilized. This paper chooses interval type-2 fuzzy sets (IT2FSs) as the preferred information expression of decision makers (DMs). After the consensus-reaching process (CRP), assessment is conducted through multiobjective optimization on the basis of ratio analysis plus full multiplicative form (MULTIMOORA). Finally, the assessment results are integrated through evidence theory to obtain the final outcome.

Figure 1.

The chart of industrial energy consumption assessment

IJSWIS.352043.f01

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