Upload dataset nigerian_energy_and_utilities_predictive_maintenance

.gitattributes

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README.md

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+
+---
+license: gpl
+dataset_name: nigerian_energy_and_utilities_predictive_maintenance
+pretty_name: Nigerian Energy & Utilities – Predictive Maintenance
+size_categories: [10K<n<1M, 1M<n<10M]
+task_categories: [time-series-forecasting, tabular-regression, anomaly-detection]
+tags: [nigeria, energy, utilities, power, grid, smart-meter, renewables]
+language: [en]
+created: 2025-10-11
+---
+
+
+    # Nigerian Energy & Utilities – Predictive Maintenance
+
+    Equipment health, failure probability (30d), RUL, and recommended action with priority.
+
+    - **[category]** Maintenance & Operations
+    - **[rows]** ~120,000
+    - **[formats]** CSV + Parquet (snappy)
+    - **[geography]** Nigeria (DisCos, substations, plants)
+
+    ## Schema
+
+    | column | dtype |
+|---|---|
+| as_of_date | object |
+| disco | object |
+| substation_id | object |
+| equipment_id | object |
+| equipment_type | object |
+| health_score | float64 |
+| failure_probability_30d | float64 |
+| rul_days | int64 |
+| recommended_action | object |
+| priority | object |
+
+    ## Usage
+
+    ```python
+    import pandas as pd
+    df = pd.read_parquet('data/nigerian_energy_and_utilities_predictive_maintenance/nigerian_energy_and_utilities_predictive_maintenance.parquet')
+    df.head()
+    ```
+
+    ```python
+    from datasets import load_dataset
+    ds = load_dataset('electricsheepafrica/nigerian_energy_and_utilities_predictive_maintenance')
+    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)

nigerian_energy_and_utilities_predictive_maintenance.csv

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nigerian_energy_and_utilities_predictive_maintenance.parquet

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