Load Lens 😎

This ML model is designed for load forecasting using Gated Recurrent Units (GRU). This user-friendly app empowers users to input their past values data, specify the number of epochs, and set the batch size for training a GRU-based model. By leveraging the GRU architecture, the app efficiently captures temporal dependencies in the data, making it ideal for accurate load forecasting. Once the model is trained, users can easily download the trained model for future use, ensuring they have a reliable tool at their fingertips for predicting load demand.

In addition to model training, this app offers a robust suite of features to enhance usability and flexibility. Users can upload a previously trained model alongside a CSV file to retrain the model, accommodating new data and improving prediction accuracy. This iterative approach ensures the model remains up-to-date with the latest trends and patterns. Furthermore, the app allows users to upload an existing model to forecast future values based on specified inputs, providing quick and precise predictions. Whether you are training a new model, retraining with additional data, or forecasting future values, this app offers a comprehensive solution for load forecasting needs.

Run Locally

Clone the project

  git clone https://github.com/soham-shee/LoadLens.git

Install dependencies

  pip install -r 'requirements.txt'

Start the server

  streamlit run App.py

To directly start it (Alternative Method)

  ./run_app.sh

Demo

https://load-lens.streamlit.app/

Acknowledgements

• Load Forecast Dataset (Panama Case Study)