Electric Load Forecasting Project

Overview

This project implements an advanced electric load forecasting system using machine learning techniques. It combines preprocessing, clustering, and machine learning models to predict electrical load patterns and consumption.

Screenshots

Dashboard View

Main dashboard showing load forecasting predictions and analytics

Clustering Analysis

Visualization of load pattern clusters

Forecast Results

Predicted vs actual load values

Documentation

Project documentation and implementation details

Project Structure

Preprocessing Module

Located in /Preprocessing/:

• preprocessing1.ipynb: Initial data cleaning and transformation

  • Data loading and validation
  • Missing value handling
  • Feature engineering
  • Basic statistical analysis

• preprocessing2.ipynb: Advanced data processing

  • Feature selection
  • Time series decomposition
  • Data normalization
  • Dataset preparation for modeling

• Clustering&ML_Training.ipynb: Model training and evaluation

  • Clustering analysis for load pattern identification
  • Random Forest model implementation
  • Model evaluation and validation
  • Performance metrics analysis

Frontend Module

Located in /frontend/:

• React-based web interface
• Interactive visualizations
• Real-time prediction display
• User-friendly dashboard for data analysis

Key Features

• Time series analysis of electrical load data
• Pattern recognition using clustering techniques
• Machine learning-based load prediction
• Interactive web interface for result visualization
• Scalable preprocessing pipeline

Model Information

The following trained models are used in this project but not included in the repository due to size constraints:

• rf_model.joblib: Random Forest model for load prediction
• kmeans.joblib: K-means clustering model for pattern identification
• pca.joblib: PCA transformation model
• scaler.joblib: Data scaling model

Getting Started

Prerequisites

• Python 3.x
• Node.js and npm (for frontend)
• Required Python packages:

  numpy
  pandas
  scikit-learn
  joblib
  matplotlib
  seaborn
  

Installation

1. Clone the repository:

   git clone https://github.com/Salman1205/Electric-load-forecasting.git
   cd Electric-load-forecasting

2. Set up the Python environment:

   pip install -r requirements.txt

3. Set up the frontend:

   cd frontend
   npm install
   npm start

Usage

1. Run the preprocessing notebooks in sequence:

   • Start with preprocessing1.ipynb
   • Follow with preprocessing2.ipynb
   • Finally, run Clustering&ML_Training.ipynb

2. Access the web interface:

   • Launch the frontend application
   • Navigate to http://localhost:3000
   • Use the dashboard to visualize and analyze results

Results and Visualizations

The project includes several visualization outputs:

• Cluster visualization plots
• Feature importance graphs
• Forecast accuracy plots
• Load pattern analysis charts

Contributing

Contributions to improve the project are welcome. Please follow these steps:

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

Contact

For access to the trained models or any queries, please contact:

• GitHub: @Salman1205

License

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

Acknowledgments

• Thanks to all contributors who helped in developing this project
• Special thanks to the open-source community for providing essential tools and libraries