Synthetic Dataset of charging processes by electric vehicles at workplace in Germany

Description

The dataset shows eight cluster groups that depict the mobility behavior of electric vehicle users in the employee context. For this purpose, 23.9 million data entries were analyzed, corresponding to 37,238 charging sessions. These data were collected over the year 2023. The 220 charging points were exclusively accessible to employees (private use case). From the data, cluster groups were derived using the Gaussian Mixture Model, and a synthetic dataset was generated through Monte Carlo sampling. The dataset consists of 8000 synthetic profiles, offering a robust scientific basis. By retaining the same statistical attributes as the empirical data, the synthetic profiles represent eight different mobility clusters, each containing 1000 entries, including full-time and part-time employees, shift workers, pool vehicle users, and opportunists. Each cluster is represented by the mean parking start hours (arrival time - in decimal hours), mean parking duration (in decimal hours), the average energy recharged, and the average charging duration, each including the cluster-specific standard deviation and median. Further information can be obtained from the upcoming publication: "Synthetic…

Description from Zenodo, shortened.

Files (20)

• PY10_3_Merge_charging_operations_locations.py1,004 byte
• PY09_3_Chronological_order_per_CP.py1.5 kB
• XLSX01_cluster_1_Part_time_morning_shift_4_h_data.xlsx41 kB
• XLSX03_cluster_3_night_shift_12_h_pool_vehicles_data.xlsx40 kB
• PY11_2_GMM_application.py4.8 kB
• PY09_2_Timezone_adjustment_per_CP.py1.2 kB
• XLSX07_cluster_7_late_shift_8_h_pool_vehicles_data.xlsx40 kB
• PY10_4_Cleaning_charging_operations.py5.2 kB
• XLSX04_cluster_4_part_time_late_shift_4_h_pool_vehicles_data.xlsx40 kB
• XLSX05_cluster_5_pool_vehicles_opportunist_data.xlsx40 kB
• PY10_2_Merge_charging_operations_of_CP.py1 kB
• PY11_1_aic_bic.py2.1 kB
• PY11_5_heat_map_application.py2.7 kB
• XLSX06_cluster_6_night_shift_8_h_data.xlsx39 kB
• XLSX08_cluster_8_day_shift_12_h_full_time_12_h_data.xlsx41 kB
• PY11_3_GMM_Energie_Charging_per_Cluster.py1.9 kB
• PY10_1_Identification_of_charging_operations_per_CP.py4.6 kB
• XLSX02_cluster_2_full_time_9_to_5_morning_shift_8_h_data.xlsx41 kB
• PY09_1_Combining_monthly_entries_per_CP.py1.5 kB
• PY11_4_K_means_application.py4.3 kB

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