基于集成学习的电动汽车充电站超短期负荷预测

李恒杰, 朱江皓, 傅晓飞, 方陈, 梁达明, 周云

Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning

LI Hengjie, ZHU Jianghao, FU Xiaofei, FANG Chen, LIANG Daming, ZHOU Yun

表3 16种基础回归器性能对比

Tab.3 Performance comparison of 16 basic regression learners

模型 MAE MSE RMSD R2 MAPE RMSE TT/s LGBM 1.4835 9.9652 3.1472 0.9271 1.4090 0.1603 0.9520 Et 1.4767 10.9566 3.2791 0.9166 1.1199 0.1542 19.9650 Rf 1.4707 11.0504 3.2915 0.9161 1.2410 0.1519 10.6240 Gbr 1.7289 11.1057 3.3130 0.9156 1.9650 0.1876 37.1450 Dt 1.7881 16.4038 3.9911 0.8900 1.4830 0.1817 1.2130 Br 3.6100 34.2069 5.8318 0.8016 7.4220 0.4518 247.2700 Ridge 3.6205 34.3932 5.8494 0.8005 7.3350 0.4535 1.9980 Ada 4.8668 37.4468 6.0925 0.7160 11.5790 0.5769 56.5100 Lasso 3.5851 38.7354 6.2008 0.7797 6.7480 0.4169 0.4880 Lr 4.0792 38.8846 6.2222 0.7782 8.4950 0.5090 29.1220 En 3.5522 39.6849 6.2760 0.7750 6.2390 0.4039 0.6320 Omp 3.4045 42.6831 6.5088 0.7602 4.9530 0.3717 1.5520 Llar 10.6004 202.9634 14.2317 -0.0014 26.0870 0.9852 0.8980 Knr 10.7520 226.0024 15.0233 -0.1179 25.1830 0.9985 9.7160 Huber 9.3039 234.1162 15.2823 -0.1545 14.4480 0.9020 10.5580 Par 14.9551 318.1632 17.6986 -0.5662 20.0780 1.3277 1.7940