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

曾金灿, 何耿生, 李姚旺, 杜尔顺, 张宁, 朱浩骏

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4 基于小时数据不同K折取样下不同模型的测算效果

Tab.4 Calculation performance of different models based on hourly data with different K-fold samplings

K折 模型方法 评估指标 eRMSE eMAPE R2 K=1 Ridge 1.480 7.210 0.870 CNN 1.520 25.190 0.859 LightGBM 1.550 7.680 0.857 CNN-LightGBM 1.450 7.060 0.876 K=2 Ridge 1.476 7.189 0.873 CNN 1.584 28.290 0.854 LightGBM 1.547 7.712 0.861 CNN-LightGBM 1.529 7.562 0.864 K=3 Ridge 1.453 7.077 0.879 CNN 1.483 27.723 0.874 LightGBM 1.536 7.674 0.864 CNN-LightGBM 1.414 6.869 0.885 K=4 Ridge 1.550 7.717 0.857 CNN 1.501 25.435 0.867 LightGBM 1.551 7.717 0.858 CNN-LightGBM 1.445 7.036 0.876 K=5 Ridge 1.462 7.136 0.873 CNN 1.506 27.291 0.866 LightGBM 1.523 7.592 0.862 CNN-LightGBM 1.434 7.008 0.878 K平均 Ridge 1.472 7.163 0.873 CNN 1.519 26.785 0.865 LightGBM 1.542 7.675 0.861 CNN-LightGBM 1.455 7.100 0.876