使用网络DEA和GA模型评估土耳其电力分配中的能源效率
1Ondokuz Mayıs University, Samsun, Türkiye. serpil.gumustekin@omu.edu.tr.
Scientific reports
|July 2, 2025
概括
这项研究使用网络数据包裹分析 (NDEA) 和遗传算法 (GA) 评估了土耳其的配电公司. 萨卡利亚EDC表现出色,在整个发电,传输和分销过程中表现出最佳性能.
科学领域:
- 能源经济学 能源经济学
- 运营研究 运营研究
- 环境可持续性 环境可持续性
背景情况:
- 评估电力分配中的能效对于可持续发展至关重要.
- 传统的方法可能无法完全捕捉到发电流程的复杂性.
- 土耳其的能源部门需要高效的运营战略.
研究的目的:
- 评估土耳其发电公司的能源效率.
- 将传统的数据包围分析 (DEA) 与网络 DEA (NDEA) 和遗传算法 (GA) 进行比较.
- 确定该行业的最佳实践和改进领域.
主要方法:
- 员工数据包装分析 (DEA) 用于初始效率评估.
- 使用网络DEA (NDEA) 来分析子过程:生成,传输和分配.
- 应用遗传算法 (GA) 用于优化输入输出配置和评估可再生能源效率.
主要成果:
- 与传统的DEA相比,网络DEA提供了更现实的效率得分.
- 萨卡利亚EDC被确定为一个基准,在所有三个子过程中实现了效率.
- 基因算法产生的效率比传统的DEA方法高约10%.
- 在2015年评估的公司中,只有Boğaziçi EDC被认为是高效的.
结论:
- 通过剖析运营阶段,NDEA提供了一种全面的能源效率方法.
- 通过提供优化见解,GA增强了效率分析.
- 调查结果强调需要有针对性的投资来提高土耳其的能源分配效率和可持续性.
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