---
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)