A data science capstone project analyzing 731 days of Nigeria's national power grid data to uncover generation trends, grid instability, and systemic deterioration between January 2023 and December 2024.
- Mean daily generation declined 8.6% year-over-year (4,019 MWh → 3,674 MWh)
- Grid variability increased 41%, indicating chronic instability
- Frequency violations occurred on 48.9% of days in 2024
- Statistical anomalies increased 5.5x from 2023 to 2024
- The 5 worst generation days in the entire dataset all occurred in Q4 2024
├── data/
│ ├── nigeria_power_genertion_raw/ # Original unmodified dataset
│ └── nigeria_power_generation_clean/ # Preprocessed and cleaned dataset
├── notebooks/
│ ├── Emmanuel_Olafisoye_Data_Cleaning_Notebook_Karatu_2nd_Semester.ipynb # Data cleaning and preparation
│ └── Emmanuel_Olafisoye_Analysis_Notebook_2nd_Semester_Karatu.ipynb # Main analysis and visualizations
├── report/
│ ├── Emmanuel_Olafisoye_Proposal_Document_Karatu_Second_Semester.pdf # Project proposal document
│ └── Emmanuel_Olafisoye_Final_Report_Karatu_Second_Semester.pdf # Full written report
└── README.md
| Document | Description |
|---|---|
| Project Proposal | Research questions, objectives, and methodology plan |
| Final Report | Complete analysis, findings, and recommendations |
| Technique | Purpose |
|---|---|
| Two-sample t-test | Confirm statistical significance of year-over-year decline |
| Cohen's d | Measure practical effect size |
| Linear regression | Quantify monthly trend slopes per year |
| Z-score detection | Identify anomalous generation days |
| Rolling averages | Smooth noise and reveal underlying trends |
| Confidence intervals | Bound the true mean generation per year |
| Metric | 2023 | 2024 | Change |
|---|---|---|---|
| Mean Daily Generation | 4,018.6 MWh | 3,674.4 MWh | -8.6% |
| Std Deviation | 451.6 MWh | 580.5 MWh | +28.5% |
| Coefficient of Variation | 11.2% | 15.8% | +41% |
| Frequency Violations | 156 days (42.7%) | 179 days (48.9%) | +23 days |
| Statistical Anomalies | 2 days | 11 days | +5.5x |
| t-statistic | — | 8.9469 | — |
| p-value | — | < 0.000001 | — |
| Cohen's d | — | 0.6618 (large) | — |
- Python
- Pandas - data manipulation
- NumPy - numerical computation
- SciPy - statistical testing
- Matplotlib / Seaborn - visualizations
- Clone the repository
git clone https://github.com/korie-cyber/nigeria-power-analysis.git
cd nigeria-power-analysis- Install dependencies
pip install pandas numpy scipy matplotlib seaborn jupyter- Open the notebooks
jupyter notebookRun Emmanuel_Olafisoye_Data_Cleaning_Notebook_Karatu_2nd_Semester.ipynb first, then Emmanuel_Olafisoye_Analysis_Notebook_2nd_Semester_Karatu.ipynb.
The full written report is available in the /report folder. It covers
the complete methodology, all 9 visualizations, statistical tests, and
recommendations for policymakers and infrastructure planners.
Emmanuel Olafisoye
AltSchool Africa - Data Science
Capstone Project | February 2026