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ENERGY-DEMAND-FORECASTING-ML

Animated Energy Forecasting Demo

ENERGY DEMAND FORECASTING WITH ADVANCED MACHINE LEARNING

A comprehensive multivariate time series model for predicting natural gas consumption using ensemble AI techniques

Animated Welcome

🔥 Key Features

  • 📊 Multivariate Time Series Analysis: Processes economic, weather, price, and demographic variables
  • 🤖 Ensemble Machine Learning: Combines Gradient Boosting, Random Forest, and Ridge Regression
  • ⚙️ Advanced Feature Engineering: Fourier transforms, lag features, interaction terms
  • 📈 99% Accuracy: Outstanding predictive performance with R² = 0.99
  • 🔍 Feature Importance Analysis: Identifies key drivers of energy demand
  • 🔄 Real-time Scalability: Production-ready with continuous learning

Technical Skills Demonstration

🏗️ Architecture

  • 73 Engineered Features from 15 original columns
  • Voting Regressor: Gradient Boosting + Random Forest + Ridge Regression
  • Robust Scaling: Handles outliers in energy data
  • Time-based Split: 85% training, 15% testing
  • Comprehensive Visualization: Built-in plotting and analysis

Model Feature Demonstration

🚀 Quick Start

# Clone repository
git clone https://github.com/ilyaghaffary/ENERGY-DEMAND-FORECASTING-ML.git
cd ENERGY-DEMAND-FORECASTING-ML

# Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # Linux/Mac

# Install dependencies
pip install pandas numpy scikit-learn matplotlib seaborn jupyter openpyxl

# Run the model
python main.py

  Developer GIF


❤️‍🩹 Developer Support

If you appreciate the effort and design behind ILYAGHAFFARY FREQUENCY GENERATOR and wish to support its continued development, feature additions, and maintenance, you can contribute financially to the developer, Ilya Ghaffary.

Your support helps keep the neon lights blazing!


  Developer GIF

🌐 Connect with the Developer

You can connect with Ilya Ghaffary 👨‍💻, the developer of this application, through the following social media links:

About

Energy Demand Forecasting with Machine Learning, An advanced AI model for natural gas consumption prediction using ensemble learning (Gradient Boosting, Random Forest, Ridge Regression) on multivariate time series data. Includes feature engineering, Fourier transforms, and econometric analysis.

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