Energy Efficiency System

Authors

• Purici Marius @markus-17
• Moisei Liviu @MoiseiLiviu
• Gilca Constantina @LadyConstantina
• Babcinetchi Egor @Egor0000
• Covalevschi Andreea @AndreeaCvl

Introduction

EES is a system that predicts the efficiency of the solar panels in producing energy for the next 7 days. The system uses Machine Learning to forecast the energy production.

Dependencies

1. The server is written in Python 3.7
2. The real data from the invertor are saved on AWS cloud
3. The ML model used is from the facebook's kats library. The installation is not required for a working solution, but may be required for model retraining and forecasting past may 2024.
4. Dependent on the subscription at api.weatherapi.com.

Installation and Testing

1. Clone the repository

  git clone https://github.com/clubmicrolab/Solar-Sense.git

2. Run the command to install all python dependencies.

  cd pbl-prediction-api
  pip install -r requirements.txt

3. Start the server.

  python main.py

4. Open http://localhost:8080/ and check out our solution.

Real Data Visualization

To visualize the real data and real time statistics from the solar panels, follow the steps below:

1. Access the link http://18.197.198.110:3000/?orgId=1
2. Login: guest
3. Password: 2023Password_for_guest_user

License

MIT