在模糊超软集的和相似度指标下,使用集成的强大决策模型进行可再生能源资源管理
Muhammad Haris Saeed1, Muhammad Saeed2, Atiqe Ur Rahman2
1Department of Mathematics, University of Management and Technology, Lahore, 54000, Pakistan.
Heliyon
|December 13, 2024
概括
这项研究引入了一种新的Fuzzy Hypersoft Set (FHS) 方法,以优化可再生能源的选择和管理. 该方法通过减少不确定性和提高效率来加强可持续能源系统的决策.
科学领域:
- 环境科学 环境科学
- 能源政策 能源政策
- 决策科学 决策科学 决策科学
背景情况:
- 全球对可再生能源需求不断增长,需要可持续的资源管理.
- 传统的能源带来环境风险,并且正在耗尽.
- 国家特定因素使最佳可再生能源选择变得复杂.
研究的目的:
- 开发一个多标准决策 (MCDM) 模型来评估和选择可再生能源系统.
- 为了解决专家判断中的不确定性,使用一种新的动态模糊超软集方法.
- 为土耳其的可再生能源部门创建一个决策支持系统.
主要方法:
- 动态模糊超软集 (FHSs) 与Entropy (EN) 和TOPSIS操作的应用.
- 纳入细节数据分析的子参数值.
- 敏感性分析用于评估标准重量影响.
主要成果:
- 与传统的评估方法相比,基于FHS的方法证明了系统性能的提高.
- 决策支持系统显示了新可再生能源项目的整体效率提高的潜力.
- 该方法有效地处理了专家评估中固有的不确定性.
结论:
- 动态模糊超软集方法为可再生能源决策支持提供了强大的框架.
- 这种方法增强了资源管理,并促进了可持续的能源实践.
- 该技术显示了在全球优化能源系统的实际适用性.
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