通过机器学习模型进行太阳能预测:对回归算法的比较分析.
Huu Nam Nguyen1, Quoc Thanh Tran1, Canh Tung Ngo2
1Institute for Hydropower and Renewable Energy, Vietnam Academy for Water Resources, Hanoi, Vietnam.
PloS one
|January 2, 2025
概括
准确的太阳能预测对于电网稳定性至关重要. 机器学习模型,特别是CatBoost,在预测受环境温度和湿度影响的太阳能发电方面表现有前途.
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
- 环境科学 环境科学
背景情况:
- 来自光伏电池板的太阳能是一种不断增长的全球趋势,具有显著的环境和社会效益.
- 太阳能发电的间歇性要求对电网稳定性,储能优化和供需平衡进行准确的预测.
- 分布式太阳能系统进一步增加了对可靠能源预测模型的需求.
研究的目的:
- 评估五种用于太阳能预测的机器学习模型的性能.
- 确定影响太阳能发电的关键气象因素.
- 确定最准确的模型来预测太阳能输出.
主要方法:
- 使用了一个包含21,045个样本的数据集.
- 采用了五种回归模型:梯度提升回归器 (GB),XGBoost,K-邻居回归器 (KNN),LightGBM和CatBoost回归器 (CatBoost).
- 输入特征包括湿度,环境温度,风速,可见度,云顶和压力. 使用R2,RMSE和MAE来评估模型准确性.
主要成果:
- CatBoost模型在太阳能预测方面表现出卓越的性能.
- 在训练期间,CatBoost实现了0.608的R2,4.478W的RMSE和3.367W的MAE.
- 在测试数据上,CatBoost的R2值为0.46,RMSE值为4.748W,MAE值为3.583W.SHAP分析显示环境温度和湿度是主要影响因素.
结论:
- CatBoost模型对于太阳能预测非常有效.
- 环境温度和湿度是太阳能发电的关键预测因素.
- 使用机器学习准确预测可以提高太阳能在电网中的集成和管理.
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