---
license: gpl
dataset_name: nigerian_energy_and_utilities_billing_payments
pretty_name: Nigerian Energy & Utilities – Billing & Payments
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 – Billing & Payments

    Monthly billing and payments with arrears and on-time payment flags.

    - **[category]** Customer Experience
    - **[rows]** ~200,000
    - **[formats]** CSV + Parquet (snappy)
    - **[geography]** Nigeria (DisCos, substations, plants)

    ## Schema

    | column | dtype |
|---|---|
| customer_id | object |
| disco | object |
| billing_month | object |
| tariff_band | object |
| kwh | float64 |
| price_ngn_kwh | float64 |
| amount_billed_ngn | float64 |
| amount_paid_ngn | float64 |
| paid_on_time | bool |
| arrears_ngn | float64 |

    ## Usage

    ```python
    import pandas as pd
    df = pd.read_parquet('data/nigerian_energy_and_utilities_billing_payments/nigerian_energy_and_utilities_billing_payments.parquet')
    df.head()
    ```

    ```python
    from datasets import load_dataset
    ds = load_dataset('electricsheepafrica/nigerian_energy_and_utilities_billing_payments')
    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)