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---
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: 2025-10-11
---
# 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)
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