Table 4 Performance comparison of six representative forecasting methods in node prediction. It is evident that the Transformer-based model, TimeXer, and the RNN-based model, LSTM, stand out with superior performance. This observation indicates that the charging data offered by UrbanEV encompasses ample temporal features.
From: UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction
Model | RMSE | MAPE | ||||
|---|---|---|---|---|---|---|
o | d | v | o | d | v | |
LO | 0.10 | 4.35 | 68.53 | 0.41 | 0.57 | 0.58 |
AR | 0.08 | 5.60 | 74.29 | 0.11 | 0.89 | 0.88 |
ARIMA | 0.13 | 5.86 | 76.41 | 0.59 | 0.89 | 0.89 |
FCNN | 0.11 | 3.62 | 55.52 | 0.54 | 0.56 | 0.57 |
LSTM | 0.09 | 3.20 | 45.14 | 0.46 | 0.52 | 0.52 |
TimeXer | 0.07 | 2.73 | 43.66 | 0.29 | 0.55 | 0.66 |
Model | RAE | MAE | ||||
|---|---|---|---|---|---|---|
o | d | v | o | d | v | |
LO | 0.79 | 0.78 | 0.78 | 0.07 | 3.17 | 51.71 |
AR | 1.06 | 1.07 | 1.07 | 0.07 | 4.51 | 59.18 |
ARIMA | 1.10 | 1.09 | 1.09 | 0.10 | 4.67 | 63.81 |
FCNN | 1.05 | 0.78 | 0.79 | 0.09 | 2.89 | 44.44 |
LSTM | 0.92 | 0.72 | 0.71 | 0.07 | 2.48 | 34.12 |
TimeXer | 0.76 | 0.70 | 0.71 | 0.05 | 2.04 | 33.81 |
