Siyaovo/south-africa-electricity-analysis
Domain:
environment and energyRecord type:
project
Creator:
Siy
Host:
# South Africa Electricity System Analysis
### Demand Forecasting & Supply Optimisation
**Supervised by Nicolas Maisonneuve-Bonteil, Deloitte | Université Paris 1 Panthéon-Sorbonne | M2 Sustainable Development Economics**
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## Overview
This project develops a quantitative analytical framework for South Africa's electricity system, combining **electricity demand forecasting** with **supply-side economic dispatch optimisation**. The analysis evaluates trade-offs between cost, reliability, and decarbonisation under alternative policy scenarios.
The case study is motivated by South Africa's ongoing energy crisis: chronic load shedding, heavy coal dependence (~80% of generation), and a structural transition toward renewables under the Just Energy Transition Partnership (JETP).
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## Project Structure
```
south-africa-electricity-analysis/
├── notebooks/
│ ├── 01_demand_forecasting.ipynb # Demand forecasting (ARIMA/SARIMAX/LSTM/TFT/Hybrid)
│ └── 02_supply_optimization.ipynb # Pyomo dispatch model & scenarios
├── data/
│ └── ESK17390.csv # Eskom hourly system data (2021–2025)
├── reports/
│ ├── south-africa-electricity-analysis.pdf # Full academic report (PDF)
│ ├── presentation_slides.pdf # Presentation slides
│ └── report_source.md # Report source (Markdown)
├── figures/
├── requirements.txt
└── README.md
```
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## Analytical Components
### 1. Demand Forecasting (`01_demand_forecasting.ipynb`)
Forecasts daily electricity demand over a 90-day out-of-sample horizon using six modelling approaches:
| Model | Type | Key feature |
|-------|------|-------------|
| ARIMA(1,0,1) | Statistical | Baseline univariate model |
| SARIMA(1,0,1)(1,1,1,7) | Statistical | Weekly seasonality |
| SARIMAX | Statistical | Exogenous regressors (outages, load shedding, pumped storage) |
| LSTM | Deep learning | Nonlinear dynamics, 30-day lookback window |
| Temporal Fusion Transformer (TFT) | …