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Updated: May 12, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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机器学习技术对光伏环境中温度和湿度预测的比较分析

Montaser Abdelsattar1, Ahmed AbdelMoety2, Ahmed Emad-Eldeen3

  • 1Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena, 83523, Egypt. Montaser.A.Elsattar@eng.svu.edu.eg.

Scientific reports
|May 5, 2025
PubMed
概括

极端梯度提升 (XGBoost) 在预测光伏 (PV) 系统的温度和湿度方面表现出色,优于其他机器学习模型. 整体方法通常显示出对这些环境预测的线性模型的优越性能.

关键词:
数据驱动的预测湿度预测的预测机器学习 机器学习光伏环境中的光伏环境.预测温度的预测

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