光伏短期功率预测大赛 代码
李家翔,武睿琦,靳晓松 2023-02-06
我们尝试的模型融合有
本次比赛,我们主要的实现方式是神经网络模型,最终的排名是52名。我们的特征工程涵盖了时间相关变量、平方项、立方项、比率、滚动SMA、滚动方差、PCA主成分、实发辐射的测试集预测值、NMF衍生变量、prophet等,而模型融合则涵盖了神经网络模型、Xgboost模型、时间序列模型以及基于概率模型的融合。
这个项目是参加国能日新的光伏短期功率预测大赛的结稿。我们的团队名为
PHotoVoltaic (phv)
,最终排名是52名。
在这个比赛中,我们尝试了一系列的特征工程和模型融合,以提高模型的性能。在特征工程方面,我们加入了时间相关变量、平方项、立方项、比率、滚动SMA、滚动方差、PCA主成分、实发辐射的测试集预测值、NMF衍生变量、prophet等;在模型融合方面,我们尝试了神经网络模型、Xgboost模型、时间序列模型以及基于概率模型的融合。
我们的实现方式主要是神经网络模型,具体见Python代码wushen.ipynb
,而Xgboost的融合则见R代码note.Rmd
。我们也使用了trelliscope
来进行EDA,交互方便,但是不适合上线部署,不便于交流。
最终,我们的模型达到了较好的效果,跑出了52名的排名。
使用trelliscope
,交互方便,但是不适合上线部署,不便于交流。
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