electricsheepafrica/nigerian_electricity_disco_performance

DISCO Performance

⚠️ SYNTHETIC DATA DISCLAIMER

This dataset contains synthetic/modeled data, not direct measurements.

• Purpose: Research, education, and methodology demonstration
• Generation: Geospatial disaggregation model using proxy indicators
• Validation: Grounded in official World Bank national data
• Limitations: State/LGA estimates are modeled, not measured
• Use with caution: Not suitable for operational decisions without validation

For official data, consult: World Bank, NERC, REA, DISCOs directly.

Dataset Description

Performance metrics for Nigeria's 11 electricity distribution companies (DISCOs) including customer numbers, collection efficiency, and losses.

Rows: 66
Columns: 9
Period: 2018-2023 (where applicable)
License: MIT

Data Quality

⭐⭐⭐ Synthetic data based on public reports

Methodology

Data Generation Process

This dataset is part of a geospatial electrification analysis project that addresses the lack of state-level electricity access data in Nigeria.

Challenge: World Bank provides only national-level access rates. No state-by-state breakdown exists.

Solution: Geospatial disaggregation model using weighted proxy indicators:

State_Access = National_Rate × Adjustment_Factor

Adjustment_Factor = (
    35% × Night-time Lights Index +
    25% × Grid Proximity Index +
    20% × Urban Population Share +
    15% × DISCO Performance Index +
    5% × Historical Baseline
)

Validation:

• State averages match national figures (< 0.1% difference)
• Adjustment factors normalized (mean = 1.0)
• Realistic bounds applied (10-98% access range)
• Urban > Rural access (consistent with known patterns)

Data Sources

• World Bank API: National electricity access rates (2018-2023)
• GADM: Administrative boundaries (37 states, 775 LGAs)
• Proxy indicators: Urbanization rates, DISCO coverage, infrastructure patterns
• Public reports: NERC quarterly reports, REA project data

Data Dictionary

Column	Type	Description	Example
disco_name	object	Disco Name	Abuja Electricity Distribution Company
year	int64	Year	2018
customers_total	int64	Customers Total	632633
customers_metered	int64	Customers Metered	293367
collection_efficiency_pct	float64	Collection Efficiency Pct	63.1
technical_losses_pct	float64	Technical Losses Pct	10.1
commercial_losses_pct	float64	Commercial Losses Pct	7.8
energy_received_mwh	float64	Energy Received Mwh	1127000.0
energy_billed_mwh	float64	Energy Billed Mwh	3295000.0

Usage

Load with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/nigerian_electricity_disco_performance")
df = dataset['train'].to_pandas()

Load with Pandas

import pandas as pd

# From Parquet (recommended)
df = pd.read_parquet("hf://datasets/electricsheepafrica/nigerian_electricity_disco_performance/nigerian_electricity_disco_performance.parquet")

# From CSV
df = pd.read_csv("hf://datasets/electricsheepafrica/nigerian_electricity_disco_performance/nigerian_electricity_disco_performance.csv")

Sample Data

                            disco_name  year  customers_total  customers_metered  collection_efficiency_pct  technical_losses_pct  commercial_losses_pct  energy_received_mwh  energy_billed_mwh
Abuja Electricity Distribution Company  2018           632633             293367                       63.1                  10.1                    7.8            1127000.0          3295000.0
Abuja Electricity Distribution Company  2019           645285             335819                       66.7                  18.8                   19.6            1273000.0          2346000.0
Abuja Electricity Distribution Company  2020           657938             311711                       67.4                  23.6                   15.9            3648000.0          2433000.0

Use Cases

• Policy research: Identify underserved areas for electrification programs
• Investment analysis: Assess market opportunities for off-grid solutions
• Academic research: Study determinants of electricity access
• Methodology validation: Compare geospatial disaggregation approaches
• SDG 7 tracking: Monitor progress toward universal energy access

Limitations

• Synthetic data: State and LGA estimates are modeled, not measured
• Time period: Limited to 2018-2023
• Granularity: No settlement-level data (requires GRID3 integration)
• Validation: Limited by availability of ground-truth data
• Simplifications: Actual electrification patterns are more complex

Citation

@dataset{nigerian_electricity_access_2025,
  title = {Nigerian Electricity Access: DISCO Performance},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  note = {Geospatial disaggregation using proxy indicators},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_electricity_disco_performance}
}

Collection

Part of the Nigeria Electricity Access collection containing 7 datasets on rural-urban electrification.

Related Datasets

• Nigeria Oil & Gas Sector
• Nigeria Energy & Utilities

Methodology Documentation

For detailed methodology, see:

• PROJECT_PLAN.md
• RESULTS_SUMMARY.md

Updates

This dataset is versioned. Check the repository for updates and corrections.

Contact

For questions, corrections, or collaboration:

• Organization: Electric Sheep Africa
• Collection: Nigeria Electricity Access

License

MIT License - Free to use with attribution. Please cite appropriately and acknowledge the synthetic nature of the data.