由人工智能驱动的能源管理系统基于犹不决的双极复杂模糊的Hamacher电力聚合运营商及其在MADM中的应用
Walid Emam1, Hafiz Muhammad Waqas2, Tahir Mahmood3,4
1Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia.
Scientific reports
|April 16, 2025
概括
这项研究引入了新的犹双极复杂模糊集 (HBCFS) 聚合运营商,用于AI能源管理. 这些先进的方法提高了能源分配和利用优化的决策准确性和可靠性.
科学领域:
- 能源管理系统 能源管理系统
- 人工智能的人工智能
- 模糊的集合理论 模糊的集合理论
背景情况:
- 基于AI的能源管理系统优化了各个部门的能源消耗.
- 现有的系统面临着不确定性和复杂数据的挑战.
- 为了提高效率,需要先进的决策模型.
研究的目的:
- 为人工智能能源管理开发新的数学聚合运算符.
- 为了解决不确定性和双极信息,使用犹的双极复杂模糊集 (HBCFS).
- 提高能源管理中的多属性决策 (MADM) 的精度和稳定性.
主要方法:
- 创建了新的HBCF Hamacher电力聚合运营商.
- 应用Hamacher t-规范和功率聚合规则.
- 进行比较分析以证明拟议运营商的优势.
主要成果:
- 拟议的运营商显著提高了AI能源管理决策的准确性和可靠性.
- 在处理复杂,不确定的和双极信息方面表现出有效性.
- 在MADM过程中提高了精度和稳定性.
结论:
- 开发的HBCFS聚合运营商为AI能源管理提供了强大的解决方案.
- 该方法显示了优化能源分配和利用的巨大潜力.
- 这项研究有助于更高效,更可靠的能源管理系统.
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