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A physically-informed interpretable ensemble learning method for distributed photovoltaic power forecasting
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DOI：10.1016/j.ijepes.2026.111571.png)
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摘要
En 中文
• 构建物理知识表面以增强模型可解释性。
• 一个泛化的集成框架提高了多样化基础模型的性能。
• 集成SHAP方法实现了对集成模型的解释。
• 在55个光伏电站上的测试验证了该方法稳健的预测性能。
Keyword:
Distributed photovoltaic power forecasting
Physics-data hybrid driven
Deep learning
Ensemble learning
Interpretable machine learning
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期刊
I
IF:
5
论文数:
661
被引数:
0
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