通过数值模型和情感神经网络对先进太阳能电池板生产力和效率进行全面分析
Ali Basem1, Serikzhan Opakhai2, Zakaria Mohamed Salem Elbarbary3
1Air Conditioning Engineering Department, Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, Iraq.
Scientific reports
|January 2, 2025
概括
这项研究将数值模型与情感人工神经网络 (EANN) 集成,以提高太阳能电池板性能预测. 这种新的方法准确地预测了能源和能量效率,降低了太阳能系统的计算成本.
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.
- 热力学和能源分析
背景情况:
- 在将数值模型与先进的机器学习集成为太阳能电池板模拟方面存在研究缺口.
- 优化太阳能电池板的电气特性和效率需要强大的预测工具.
研究的目的:
- 开发和评估一种新的方法,将数值建模与情感人工神经网络 (EANN) 结合起来,用于太阳能电池板性能分析.
- 模拟和优化标准200W太阳能电池的电气特性和效率.
- 在不同的条件下建立用于实时效率计算的数学方程.
主要方法:
- 采用数字模型与新型EANN相结合,模拟电气特性 (例如,开通电压,短路电流).
- 开发了用于计算能量和能量效率的数学方程.
- 对操作,电气和环境参数 (太阳辐射,风速,环境温度) 进行实证观察.
- 根据制造商数据和实验结果验证了模型.
主要成果:
- 能源效率在10.34%至14.00% (平均13.6%) 之间,能量效率在13.57%至16.41% (平均15.70%) 之间.
- EANN模型证明了在降低计算成本的情况下预测能量,能量和功率的可行性.
- 综合方法提高了模拟准确性和预测能力.
结论:
- 将数值建模与EANN集成,为太阳能电池板性能预测提供了一种可行且计算效率高的方法.
- 开发的方程可以实时计算效率,改善太阳能系统的设计和部署.
- 在各种环境条件下的实证验证可以提高太阳能电池板性能模型的预测准确度.
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