基于卷积神经网络与轻量级梯度提升树组合模型的电力行业短期以电折碳方法

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A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM

ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun

表3 基于日度数据不同K折取样下不同模型的测算效果

Tab.3 Calculation performance of different models based on daily data with different K-fold samplings

K折 模型方法 评估指标 eRMSE eMAPE R2 K=1 Ridge 0.315 6.122 0.852 CNN 0.316 21.749 0.852 LightGBM 0.320 6.348 0.847 CNN-LightGBM 0.293 5.687 0.872 K=2 Ridge 0.331 6.785 0.832 CNN 0.348 22.810 0.814 LightGBM 0.337 7.046 0.826 CNN-LightGBM 0.331 6.857 0.832 K=3 Ridge 0.326 6.503 0.835 CNN 0.378 24.190 0.778 LightGBM 0.337 6.914 0.823 CNN-LightGBM 0.300 5.929 0.860 K=4 Ridge 0.339 6.805 0.830 CNN 0.346 23.370 0.823 LightGBM 0.341 7.077 0.828 CNN-LightGBM 0.310 6.178 0.858 K=5 Ridge 0.335 6.577 0.819 CNN 0.356 21.831 0.796 LightGBM 0.340 6.930 0.813 CNN-LightGBM 0.323 6.555 0.832 K平均 Ridge 0.329 6.559 0.833 CNN 0.349 22.790 0.812 LightGBM 0.335 6.863 0.827 CNN-LightGBM 0.311 6.241 0.851