pmashambanhaka/south-africa-energy-forecasting

End-to-end machine learning project for forecasting and analysing South African electricity demand using Python, data analysis, and predictive modelling. # South African Energy Forecasting & Analytics ## Project Overview This project focuses on analysing and forecasting electricity demand in South Africa using machine learning and data science techniques. The goal is to build an end-to-end data science project that demonstrates: - Data collection and cleaning - Exploratory data analysis - Feature engineering - Forecasting and predictive modelling - Model evaluation - Visualisation of results - Clear technical documentation ## Problem Statement Electricity demand forecasting is important for planning, grid stability, energy management, and operational decision-making. This project explores how historical electricity, time-based, and related external data can be used to understand demand patterns and develop forecasting models. ## Objectives - Analyse electricity demand patterns over time - Identify trends, seasonality, and anomalies - Build baseline forecasting models - Compare model performance - Present findings through visualisations and a written case study ## Planned Tools and Technologies - Python - pandas - NumPy - Matplotlib - scikit-learn - Jupyter Notebook - Git and GitHub ## Project Structure ```text south-africa-energy-forecasting/ │ ├── data/ │ ├── raw/ │ └── processed/ │ ├── notebooks/ │ └── 01_exploratory_data_analysis.ipynb │ ├── src/ │ ├── data_preparation.py │ ├── features.py │ └── modelling.py │ ├── reports/ │ └── figures/ │ ├── README.md ├── requirements.txt └── .gitignore