Table 2 Data statistics of the UrbanEV Dataset. This table presents data across three dimensions: EV, Weather, and Others.

From: UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction

Feature

Max

Mean

Median

Min

Q1

Q3

Std

EV

Day

o (%)

1

0.24

0.21

0

0.14

0.31

0.17

d (hour)

175.83

9.64

5.00

0.00

1.50

12.50

12.78

v (kWh)

13637.50

180.61

37.62

0.00

10.50

111.42

593.53

pe (CNY)

1.80

0.98

0.99

0.23

0.86

1.10

0.18

ps (CNY)

1.45

0.70

0.76

0.00

0.70

0.76

0.15

Night

o (%)

1.00

0.30

0.26

0.00

0.16

0.40

0.19

d (hour)

207.58

13.58

6.00

0.00

1.92

16.58

19.54

v (kWh)

16732.50

275.72

46.08

0.00

12.25

147.00

968.24

pe (CNY)

1.80

0.93

0.94

0.23

0.79

1.06

0.23

ps (CNY)

1.45

0.73

0.76

0.00

0.74

0.76

0.14

Weather

Ta (°C)

34.70

21.18

21.40

8.30

16.40

26.30

5.92

P (mmHg)

773.80

762.49

762.40

751.10

759.90

765.50

4.18

h (%)

97.00

69.33

72.00

21.00

58.00

82.00

16.49

Others

Number of Piles

373.00

63.75

48.00

4.00

24.00

84.00

56.63

Distance (meter)

78680.32

21972.31

20476.39

0.00

12184.82

29764.97

12721.15

Number of Neighbors

10.00

4.36

4.00

0.00

3.00

6.00

1.82

  1. First, UrbanEV provides EV charging-related data, i.e., occupancy ratio (o), charging duration (d), charging volume (v), and charging price (including electricity price pe and service fee ps), with statistics at the traffic zone level. In the weather dimension, it offers three representative features that have the potential to influence charging behaviors, namely air temperature (Ta), atmospheric pressure (P), and relative humidity (h) across the entire study area. Lastly, information on pile number, adjacency, and distance within or between traffic zones is also incorporated into the dataset.