⚡ Energy Consumption Forecasting & Optimization

A comprehensive machine learning project for energy demand forecasting and distribution optimization using advanced ML algorithms and interactive visualization.

🎯 Project Overview

This project implements an end-to-end machine learning pipeline for energy consumption forecasting and optimization. It combines multiple ML models, optimization algorithms, and an interactive dashboard to provide comprehensive energy management insights.

Key Features

• 🔮 Advanced Forecasting: Multiple ML models (Linear Regression, Random Forest, XGBoost, LSTM, GRU)
• ⚙️ Energy Optimization: Linear programming, genetic algorithms, and reinforcement learning approaches
• 📊 Interactive Dashboard: Real-time visualization with Streamlit
• 📈 Comprehensive Analysis: Exploratory data analysis with seasonal patterns and correlations
• 🎛️ Model Comparison: Performance metrics and feature importance analysis
• 🔧 Modular Architecture: Clean, extensible codebase with proper documentation

🏗️ Project Structure

Energy_Forecasting/
├── src/
│   ├── data/
│   │   ├── collect_data.py      # Data collection and synthetic generation
│   │   ├── preprocess_data.py   # Data preprocessing and feature engineering
│   │   └── utils.py             # Data utility functions
│   ├── models/
│   │   ├── baseline_models.py   # Traditional ML models
│   │   ├── time_series_models.py # Deep learning models (LSTM/GRU)
│   │   └── evaluation.py        # Model evaluation framework
│   └── optimization/
│       └── energy_optimizer.py  # Energy distribution optimization
├── notebooks/
│   └── 01_exploratory_data_analysis.ipynb # Comprehensive EDA
├── dashboard/
│   └── app.py                   # Streamlit interactive dashboard
├── data/
│   ├── raw/                     # Raw data files
│   └── processed/               # Processed data files
├── models/
│   └── trained/                 # Saved model files
├── requirements.txt             # Python dependencies
├── .env.example                 # Environment variables template
└── README.md                    # Project documentation

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/yourusername/energy-forecasting.git
   cd energy-forecasting

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   cp .env.example .env
   # Edit .env with your configuration

Running the Project

1. Data Collection and Preprocessing

# Generate synthetic energy data
python src/data/collect_data.py

# Preprocess the data
python src/data/preprocess_data.py

2. Exploratory Data Analysis

# Open the Jupyter notebook
jupyter notebook notebooks/01_exploratory_data_analysis.ipynb

3. Model Training

# Train baseline models
python src/models/baseline_models.py

# Train time series models
python src/models/time_series_models.py

# Evaluate all models
python src/models/evaluation.py

4. Energy Optimization

# Run optimization algorithms
python src/optimization/energy_optimizer.py

5. Interactive Dashboard

# Launch the Streamlit dashboard
streamlit run dashboard/app.py

The dashboard will be available at http://localhost:8501

🤖 Models Implemented

Baseline Models

• Linear Regression
• Random Forest Regressor
• XGBoost

Advanced Models

• LSTM (Long Short-Term Memory)
• GRU (Gated Recurrent Unit)
• Prophet for time series

Optimization

• Reinforcement Learning for energy allocation
• Linear Programming for cost optimization

📈 Performance Metrics

• RMSE (Root Mean Squared Error)
• MAE (Mean Absolute Error)
• MAPE (Mean Absolute Percentage Error)
• R² Score

🎛️ Dashboard Features

• Real-time energy consumption forecasts
• Historical trend analysis
• Seasonal pattern visualization
• Optimization recommendations
• Anomaly detection alerts
• Cost analysis and savings projections

🔧 Configuration

Create a .env file with:

OPENWEATHER_API_KEY=your_api_key_here
DB_CONNECTION_STRING=your_db_string_here
DASHBOARD_PORT=8501

📚 Notebooks

1. 01_exploratory_data_analysis.ipynb - Data exploration and visualization
2. 02_data_preprocessing.ipynb - Data cleaning and feature engineering
3. 03_baseline_models.ipynb - Simple ML models
4. 04_advanced_models.ipynb - Deep learning and time series models
5. 05_optimization.ipynb - Energy distribution optimization
6. 06_model_evaluation.ipynb - Comprehensive model comparison

🚢 Deployment

Docker Deployment

docker build -t energy-forecast .
docker run -p 8501:8501 energy-forecast

Cloud Deployment

• Deploy on AWS, GCP, or Azure
• Use containerized deployment with Kubernetes
• Set up CI/CD pipelines

🔮 Future Extensions

• IoT sensor integration
• Real-time data streaming
• Carbon footprint estimation
• Multi-region forecasting
• Advanced anomaly detection
• Mobile app interface

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Contact

Your Name - your.email@example.com Project Link: https://github.com/yourusername/energy-forecasting

🙏 Acknowledgments

• UCI Machine Learning Repository
• UK National Grid
• OpenWeatherMap API
• Scikit-learn and TensorFlow communities