Table 3 Performance comparison of representative forecasting methods on the spatial-temporal occupancy prediction task using the UrbanEV dataset. The result showcases that models incorporating both spatial and temporal patterns can achieve superior predictive accuracy. This observation suggests that the UrbanEV dataset exhibits pronounced spatiotemporal dependencies within EV charging data.
From: UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction
Model | RMSE(×10−2) | MAPE(%) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
3h | 6h | 9h | 12h | Average | 3h | 6h | 9h | 12h | Average | |
LO | 9.75 | 12.52 | 14.65 | 15.45 | 13.09 | 25.39 | 39.07 | 50.92 | 56.70 | 43.02 |
AR | 13.08 | 13.00 | 12.89 | 12.67 | 12.91 | 58.30 | 59.12 | 60.93 | 61.74 | 60.02 |
ARIMA | 13.76 | 13.88 | 13.44 | 12.79 | 13.47 | 58.63 | 58.89 | 59.56 | 59.10 | 59.05 |
FCNN | 9.47 | 10.74 | 10.95 | 9.79 | 10.24 | 40.59 | 50.12 | 52.67 | 46.22 | 47.40 |
LSTM | 9.37 | 10.96 | 11.05 | 9.74 | 10.28 | 36.17 | 46.44 | 49.81 | 43.54 | 43.99 |
GCN | 8.91 | 10.63 | 10.93 | 10.08 | 10.14 | 39.93 | 50.32 | 51.76 | 46.92 | 47.23 |
GCNLSTM | 8.41 | 9.67 | 10.65 | 9.39 | 9.53 | 35.96 | 45.01 | 50.12 | 43.26 | 43.59 |
ASTGCN | 9.15 | 10.61 | 10.92 | 9.83 | 10.13 | 35.67 | 46.02 | 49.37 | 47.52 | 44.64 |
TimesNet | 9.00 | 9.59 | 9.92 | 9.64 | 9.54 | 31.65 | 35.19 | 37.58 | 36.37 | 35.20 |
TimeXer | 8.32 | 9.38 | 9.89 | 9.39 | 9.24 | 26.13 | 33.20 | 36.47 | 35.14 | 32.74 |
Model | RAE( × 10−2) | MAE( × 10−2) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
3h | 6h | 9h | 12h | Average | 3h | 6h | 9h | 12h | Average | |
LO | 36.62 | 54.05 | 68.08 | 74.05 | 58.20 | 4.91 | 7.26 | 9.17 | 9.98 | 7.83 |
AR | 67.54 | 67.07 | 66.66 | 65.65 | 66.73 | 8.99 | 8.98 | 8.96 | 8.83 | 8.94 |
ARIMA | 70.82 | 70.90 | 69.21 | 65.78 | 69.18 | 9.46 | 9.52 | 9.31 | 8.84 | 9.28 |
FCNN | 45.62 | 54.62 | 56.02 | 49.08 | 51.34 | 6.11 | 7.33 | 7.54 | 6.62 | 6.90 |
LSTM | 43.51 | 54.57 | 55.85 | 48.27 | 50.55 | 5.82 | 7.32 | 7.52 | 6.50 | 6.79 |
GCN | 45.61 | 55.69 | 57.34 | 52.64 | 52.82 | 6.11 | 7.48 | 7.73 | 7.11 | 7.11 |
GCNLSTM | 41.79 | 50.13 | 55.86 | 48.12 | 48.97 | 5.59 | 6.73 | 7.52 | 6.48 | 6.58 |
ASTGCN | 42.70 | 52.90 | 55.32 | 49.91 | 50.21 | 5.71 | 7.10 | 7.45 | 6.73 | 6.75 |
TimesNet | 40.78 | 45.05 | 47.28 | 45.86 | 44.74 | 5.48 | 6.03 | 6.31 | 6.12 | 5.99 |
TimeXer | 35.01 | 42.24 | 46.43 | 44.12 | 41.95 | 4.71 | 5.66 | 6.20 | 5.89 | 5.61 |
