全面评估机器学习模型,以预测光伏系统的最大功率
Samir A Hamad1, Mohamed A Ghalib1, Amr Munshi2
1Process Control Technology Department, Faculty of Technology and Education, Beni-Suef University, Beni Suef, Egypt.
Scientific reports
|March 29, 2025
概括
机器学习模型可以通过准确预测最大功率点 (MPP) 来优化独立的光伏 (PV) 系统. 决策树回归 (DTR) 在不同条件下预测光伏系统参数方面表现优异.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习应用 机器学习应用
- 电力电子 电力电子 电力电子
背景情况:
- 光伏 (PV) 系统表现出非线性发电,挑战有效的数据管理.
- 波动的天气条件需要先进的方法来优化光伏系统的性能.
- 机器学习 (ML) 提供了一种有前途的方法,通过跟踪其最大功率点 (MPP) 来提高光伏系统的效率.
研究的目的:
- 开发和评估用于跟踪独立光伏系统的MPP的机器学习模型.
- 为了比较各种ML算法在预测光伏系统参数中的预测精度.
- 确定影响光伏系统优化ML模型性能的关键因素.
主要方法:
- 探索ML算法,包括线性回归 (LR),回归 (RR),拉索回归 (Lasso R),贝叶斯回归 (BR),决策树回归 (DTR),梯度增强回归 (GBR) 和人工神经网络 (ANN).
- 利用光伏单元的技术规格,辐射率和温度数据来预测最大功率,电流和电压.
- 在100千瓦太阳能电池板上进行模拟;使用根平均平方误差 (RMSE),确定系数 (R2) 和平均绝对误差 (MAE) 评估性能.
主要成果:
- 决策树回归 (DTR) 在预测最大电流 (Im),电压 (Vm) 和功率 (Pm) 方面明显优于其他经过测试的ML算法.
- DTR模型实现了高精度,RMSE,MAE和R2值为Im的0.006,0.004和0.9999;Vm的0.015,0.0036和0.9999;Pm的2.36,0.871和0.9999;而Im的0.006,0.004和0.9999的RMSE,MAE和R2值为0.006和0.0099;Vm的0.015,0.0036和0.9999;Pm的2.36和0.871和0.9999的RMSE和MAE.
- 功能重要性分析表明,培训数据集大小,操作条件,模型类型和数据预处理显著影响预测准确性.
结论:
- 机器学习,特别是决策树回归,通过准确跟踪MPP,为优化独立光伏系统提供了有效的解决方案.
- 开发的ML模型可以预测基本的光伏参数,并为提高转换器工作周期的调整提供信息,以改善能源采集.
- 进一步的研究应考虑数据集大小,操作条件和预处理技术对光伏能源系统预测性能增强的影响.
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