墨西哥太阳能光伏电池板生产:一种新的机器学习方法
Francisco Javier López-Flores1, César Ramírez-Márquez1, Eusiel Rubio-Castro2
1Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Av. Francisco J. Múgica, S/N, Ciudad Universitaria, Edificio V1, Morelia, Mich., 58060, Mexico.
Environmental research
|December 31, 2023
概括
墨西哥显示了太阳能光伏电池板制造的巨大潜力. 先进的人工神经网络模型预测植物指标,揭示了扩大生产的显著经济和环境效益.
科学领域:
- 可再生能源系统可再生能源系统
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 在全球太阳能光伏 (PV) 生产 (,晶片,电池,模块) 中,拉丁美洲的代表性不足.
- 墨西哥拥有适合光伏电池板制造的独特品质.
- 扩大光伏生产提供了经济,社会和环境方面的优势.
研究的目的:
- 评估墨西哥广泛生产太阳能光伏电池板的潜力.
- 开发和验证使用人工神经网络 (ANN) 对光伏电厂指标的先进预测模型.
- 分析墨西哥光伏电池板生产对经济和环境的影响.
主要方法:
- 开发一个与非线性编程框架 (Pyomo) 集成的先进的ANN模型.
- 为单个光伏工厂创建替代模型,包括生产时间表.
- 广泛的模拟和精细的计算用于模型验证和性能分析.
- 对ANN模型进行超参数优化,实现0.99.9以上的R平方值.
主要成果:
- 安恩模型准确地预测了光伏电厂的指标,其R平方>0.99.
- 太阳能电池板生产显著消耗水和能源,但排放量低于传统发电厂.
- 模块生产提供了最高的利率 (35.7%),而多晶生产则较低 (13.0%).
- 由于复杂的过程,细胞生产是最耗能的阶段 (39.7%).
结论:
- 墨西哥在扩大光伏电池板生产方面有很大的潜力.
- 集成机器学习和优化模型为可持续能源部门的资源配置提供了一种新的方法.
- 尽管光伏生产资源密集,但它提供了一条通往更绿色未来的道路,带来了显著的经济效益.
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