Open Access

ARTICLE

Evaluation Model for Energy Efficiency of Factory Workshop Based on DSR and Fuzzy Borda

Shengjie Yang^1,2, Zijian Zhu^1,*, Xu Ouyang³

1 HDHL Electrical and Information Technology, Co., Ltd., Changsha, 410205, China
2 College of Computer Science, Hunan University of Technology and Business, Changsha, 410205, China
3 College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

* Corresponding Author: Zijian Zhu. Email:

Energy Engineering 2025, 122(3), 1073-1092. https://doi.org/10.32604/ee.2025.060293

Received 29 October 2024; Accepted 20 January 2025; Issue published 07 March 2025

Abstract

In the context of advancing towards dual carbon goals, numerous factories are actively engaging in energy efficiency upgrades and transformations. To accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences, it is crucial to conduct comprehensive evaluations of the energy performance across various workshops. Therefore, this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response (DSR) framework combined with the fuzzy BORDA method. Firstly, an in-depth analysis of the relationships between different energy efficiency indicators was conducted. Based on the DSR model, evaluation criteria were selected from three dimensions—drive factors, state characteristics, and response measures—to establish a robust energy efficiency indicator system. Secondly, three distinct assessment techniques were selected: Grey Relational Analysis (GRA), Entropy Weight Method (EWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) forming a diversified set of evaluation methods. Subsequently, by introducing the fuzzy BORDA method, a comprehensive energy efficiency evaluation model was developed, aimed at quantitatively ranking the energy performance status of each workshop. Using a real-world factory as a case study, applying our proposed evaluation model yielded detailed scores and rankings for each workshop. Furthermore, post hoc testing was performed using the Spearman correlation coefficient, revealing a statistic value of 10.209, which validates the effectiveness and reliability of the proposed evaluation model. This model not only assists in identifying underperforming workshops within the factory but also provides solid data support and a decision-making basis for future energy efficiency optimization strategies.

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