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在热带环境下对不同光伏技术进行比较分析

V Femin¹, R Veena¹, M I Petra¹

¹Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE, 1410, Brunei Darussalam.

Scientific reports

|May 11, 2025

概括

在热带条件下比较了六种太阳能光伏技术. 无形和带有内在薄层 (HIT) 的异质连接显示出优越的性能,而铜 (CIS) 则具有最小的功率坡道.

科学领域:

可再生能源可再生能源是可再生能源.
太阳能光伏发电是如何实现的
材料科学材料科学材料科学

背景情况:

由于环境因素,热带地区对太阳能光伏 (PV) 性能提出了独特的挑战.
评估各种光伏技术对于优化这些气候中的太阳能部署至关重要.
了解电力生产波动对于电网集成和稳定性至关重要.

研究的目的:

为了在热带条件下比较六种不同的太阳能光伏技术的现场性能.
分析这些光伏系统的电力生产变化和升级行为.
使用机器学习开发用于光伏系统升级的预测模型.

主要方法:

利用了来自1.2兆瓦实验太阳能发电场的三年性能数据.
使用标准指数评估性能:数组收益率,参考收益率,捕获损失,性能比率和效率比率.
采用概率模型 (通用物流分布) 和机器学习 (ANN,SVM,kNN) 来分析动力坡道.

主要成果:

无形和HIT光伏系统表现出更高的性能和效率比率,捕获损失较低.
铜 (CIS) 太阳能系统显示出最少的功率坡道,而HIT系统显示出最多的波动.
机器学习模型在预测光伏系统升级时实现了超过96%的正常化根平均平方误差 (NRMSE).

结论:

无形和HIT技术对热带太阳能光伏应用具有前景.
太阳能光伏系统的升级特性有很大差异,影响了电网管理策略.
使用机器学习准确预测功率波动对于太阳能发电场的设计和运行是可行的和有价值的.

关键词:

一个年龄,一个年龄.阵列产出率阵列产出率捕获损失捕获损失效率比率是一个效率比率.综合物流分布的一般化物流分布.机器学习是机器学习.绩效比率是指绩效比率是指性能比率.电力坡道上的电力坡道.参考收益率是指参考收益率.在SVM中,SVM是SVM.太阳能是太阳能中的一种.kNN 在线观看

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