一个增强的神经网络算法及其用于数值优化和光伏模型的参数提取的应用
Aining Chi1, Seyedali Mirjalili2,3,4, Yiying Zhang5
1School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, 212003, China.
Scientific reports
|February 4, 2026
概括
一个增强的神经网络算法 (ENNA) 有效地提取光伏 (PV) 模型的参数,提高太阳能转换效率. 与现有的光伏系统优化metaheuristics相比,ENNA表现出卓越的性能.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 计算优化计算优化
背景情况:
- 光伏 (PV) 系统对于太阳能转换至关重要.
- 优化光伏系统需要从光伏模型中精确地提取参数.
- 现有的方法在高效准确地估计这些参数方面面临挑战.
研究的目的:
- 为准确的光伏模型参数提取提出一个增强的神经网络算法 (ENNA).
- 为了提高太阳能能源转换的效率和性能.
- 为可再生能源中复杂的优化问题提供一种新的计算方法.
主要方法:
- 开发ENNA,采用一种新的转移运营商,并采用三个学习策略.
- 整合干扰和精英运营商,以利用人口信息.
- 应用ENNA对基准函数和三个光伏模型的应用:单二极管模型 (SDM),双二极管模型 (DDM) 和光伏模块模型 (PVM).
主要成果:
- 恩纳实现了0.00098602 (SDM),0.000982485 (DDM) 和0.00242507 (PVM) 的最佳根平均平方误差.
- 在数值比较,排名和收方面,ENNA的表现优于其他10个元启发算法.
- 在光伏模型参数估计方面表现出色.
结论:
- ENNA是一种高效的算法,用于估计光伏模型中的未知参数.
- 拟议的方法显著提高了光伏系统的优化和模拟.
- 恩纳为提高太阳能转换效率提供了强大而高效的解决方案.
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