esinam-kekele/ghana-energy-demand-forecast

# ⚡ Ghana Energy Demand Forecasting --- ## 🌍 Problem Statement Reliable electricity is essential for economic growth, healthcare, education, and industrial productivity. However, electricity demand is highly dynamic and influenced by: - Weather conditions 🌦️ - Seasonal patterns 📅 - Human activity cycles 🏙️ These fluctuations make **grid planning and energy distribution challenging**. This project builds **machine learning and deep learning models** to forecast electricity demand using: - Historical energy consumption - Weather data - Calendar-based features The goal is to enable **accurate short-term forecasting for better energy planning and smarter grid operations in Ghana**. --- ## 🎯 Objectives - 🔮 Forecast electricity demand at multiple time horizons - 📊 Compare statistical, machine learning, and deep learning models - 🌦️ Evaluate performance under seasonal variations - 🧠 Improve interpretability for decision-making --- ## 🧠 Models Used ### 📉 Statistical Model - ARIMA ### 🤖 Machine Learning - XGBoost ### 🧬 Deep Learning - LSTM - Temporal Fusion Transformer (TFT) --- ## 📈 Project Workflow ```text 📊 Data Collection ↓ 🧹 Data Cleaning & EDA ↓ ⚙️ Feature Engineering ↓ 🧠 Model Training & Comparison ↓ 📉 Evaluation & Forecasting ↓ 📊 Dashboard / Visualization ↓ 📄 Technical Report ``` --- ## 🌟 Impact Improved energy demand forecasting can contribute to: - ⚡ Better energy planning - 💰 Reduced operational costs - 🔌 Improved grid reliability - 🌱 Sustainable energy management - 🏙️ Smarter infrastructure planning --- ## 🧰 Skills Demonstrated - Time series forecasting - Feature engineering for temporal data - Data preprocessing & EDA - XGBoost modeling - Deep learning (LSTM, TFT) - Model explainability - Experiment tracking --- ## 📚 Books & Resources ### 📘 Time Series Fundamentals - Forecasting: Principles and Practice otexts.com --- ### 🔥 Deep Learning Frameworks - PyTorch Forecasting Documentation pytorch-forecasting.r …