metadata
license: gpl
dataset_name: nigerian_energy_and_utilities_ev_charging_sessions
pretty_name: Nigerian Energy & Utilities – EV Charging Sessions
size_categories:
- 10K<n<1M
- 1M<n<10M
task_categories:
- time-series-forecasting
- tabular-regression
- other
tags:
- nigeria
- energy
- utilities
- power
- grid
- smart-meter
- renewables
language:
- en
created: 2025-10-11T00:00:00.000Z
# Nigerian Energy & Utilities – EV Charging Sessions
EV charging session logs across Nigeria with connectors, energy, pricing, and geo.
- **[category]** Emerging & Advanced
- **[rows]** ~120,000
- **[formats]** CSV + Parquet (snappy)
- **[geography]** Nigeria (DisCos, substations, plants)
## Schema
| column | dtype |
|---|---| | station_id | object | | session_id | object | | disco | object | | start_time | object | | end_time | object | | duration_min | int64 | | connector_type | object | | pricing_scheme | object | | energy_kwh | float64 | | power_kw_avg | float64 | | price_ngn_kwh | float64 | | amount_ngn | float64 | | lat | float64 | | lon | float64 |
## Usage
```python
import pandas as pd
df = pd.read_parquet('data/nigerian_energy_and_utilities_ev_charging_sessions/nigerian_energy_and_utilities_ev_charging_sessions.parquet')
df.head()
```
```python
from datasets import load_dataset
ds = load_dataset('electricsheepafrica/nigerian_energy_and_utilities_ev_charging_sessions')
ds
```
## Notes
- Data generated with Nigeria-specific parameters (DisCos, tariff bands, 50 Hz grid)
- Time-of-use shapes and seasonal/weather effects included where applicable
- Values are internally consistent (e.g., kWh ~ kW*h; voltage/current ~ power)