一个新的算法多属性决策框架用于评估能源系统,使用粗略近似的超软集
Muhammad Abdullah1, Khuram Ali Khan1, Jaroslav Frnda2
1Department of Mathematics, University of Sargodha, Sargodha, 40100, Pakistan.
Heliyon
|December 11, 2024
概括
本研究引入了一个新的超软粗集 (HSRS) 框架,用于选择最佳能源系统 (ESS). 高频传输系统有效地处理复杂的数据和不确定性,从而使电力赤字的决策更加稳健.
科学领域:
- 决策科学 决策科学 决策科学
- 信息理论 信息理论
- 能源系统分析 能源系统分析
背景情况:
- 选择最佳能源系统 (ESS) 是一个复杂的过程,涉及法律,经济,环境和可行性因素.
- 传统的决策框架 (模糊,软集) 在ESS选择中与数据复杂性,完整性和不确定性作斗争.
- 现有的方法往往不足以处理粗略数据,并确保对电力赤字的可靠决策.
研究的目的:
- 引入一个新的理论框架,超软粗集 (HSRS),整合粗集和超软集概念.
- 开发一个严格的算法策略来评估ESS可行性,使用HSRS操作.
- 在不确定性和不完整数据下,解决最佳能源系统选择 (ESS) 现有模型的局限性.
主要方法:
- 将粗略的集合理论用于模糊性和不确定性与超软集合理论用于不完整数据分析的整合.
- 基本概念的表征,近似空间,下/上近似和HSRS框架内的操作.
- 基于HSRS的ESS可行性评估的新型算法策略的开发和应用.
主要成果:
- 拟议的HSRS框架显示了增强的多功能性,区分能力和适合处理数据异常的适用性.
- 在巴基斯坦的实践应用成功地确定了理想的ESS,验证了算法的适应性和有效性.
- 与最佳能源系统选择的现有方法相比,HSRS方法被证明更强大.
结论:
- 超软粗集 (HSRS) 框架为能源系统选择中的复杂决策提供了强大而强大的分析工具.
- HSRS提供了一种优越的方法来解决数据的不确定性和不完整性,这对于解决电力短缺至关重要.
- 该研究验证了HSRS在现实世界能源系统优化挑战中的实际实用性和增强性能.
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