electricsheepafrica/nigerian_electricity_offgrid_solutions

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Off-Grid Solutions

⚠️ 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

Off-grid electrification installations including solar home systems, mini-grids, and standalone solar across Nigeria.

Rows: 1,000
Columns: 8
Period: 2018-2023 (where applicable)
License: MIT

Data Quality

⭐⭐⭐ Synthetic data representing market patterns

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
solution_id	object	Solution Id	OFFGRID-00001
solution_type	object	Solution Type	solar_home_system
provider	object	Provider	Daystar Power
state	object	State	Benue
households_served	int64	Households Served	19
capacity_kw	float64	Capacity Kw	1.7
installation_year	int64	Installation Year	2018
status	object	Status	operational

Usage

Load with Hugging Face Datasets

from datasets import load_dataset

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

Load with Pandas

import pandas as pd

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

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

Sample Data

  solution_id     solution_type                  provider                     state  households_served  capacity_kw  installation_year      status
OFFGRID-00001 solar_home_system             Daystar Power                     Benue                 19         1.70               2018 operational
OFFGRID-00002         mini_grid                     Lumos                      Kogi                196       113.75               2018 operational
OFFGRID-00003 solar_home_system Green Village Electricity Federal Capital Territory                 39         4.28               2019 operational

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: Off-Grid Solutions},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  note = {Geospatial disaggregation using proxy indicators},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_electricity_offgrid_solutions}
}

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.