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Energy Trading marketplace between P2P and P2G or G2P. P2P is between one nation’s grid to another nation’s grid. P2G or G2P is between prosumers to grid and vice versa.

Solidity 0.79% JavaScript 32.39% Jupyter Notebook 57.30% HTML 0.72% CSS 7.96% TypeScript 0.84%
blockchain-technology machine-learning nodejs-server reactjs smart-contracts web-development web3 hacktoberfest digitalocean hacktoberfest2023 javascript opensource solidity

eco_grid_h23's Introduction

Eco-GRiD

About The Project

Energy Trading marketplace between P2P and P2G or G2P.P2P is between one nation’s grid to another nation’s grid. P2G or G2P is between prosumers to grid and vice versa.

This project encompasses Machine Learning, Web Development, and Web3 components, aligning well with your expertise. Please consider suggesting additional features or ideas. If you have any questions or require clarification on how the project is executed, please don't hesitate to reach out via GitHub issues or on LinkedIn. Your feedback is highly valued.

The features of our product are

  • (✓) Cross-Border P2P Trading: Facilitates renewable energy exchange between different nations' grids.
  • (✓) P2G and G2P Transactions: Enables prosumers to sell excess energy to the grid and vice versa.
  • (✓) Blockchain Trust: Utilizes blockchain for secure, transparent, and immutable transaction records.
  • (✓) Prosumer Empowerment: Empowers prosumers to actively engage in the energy market.
  • (✓) Real-time Monitoring: Monitors energy transactions and grid performance in real-time.
  • (✓) User can redeem their tokens anytime / Burn Tokens Implementation and Automated Settlements: Automates payment settlements for energy transactions.

Team Members

  • Neeraj Choubisa (Team Lead)
  • Sneha Gupta
  • Chirag Mittal
  • Rakesh Roshan
  • Yash Dua

THIS REPO IS READY FOR HacktoberFest-23 You can start contributing !!!

Use this project to make your first contribution to an open source project on GitHub. Practice making your first pull request to a public repository before doing the real thing!

This repository is open to all members of the GitHub community. Any member can contribute to this project.

What is Hacktoberfest?

A month-long celebration from October 1st - 31st sponsored by Digital Ocean and GitHub to get people involved in Open Source. Create your very first pull request to any public repository on GitHub and contribute to the open source developer community.

https://hacktoberfest.digitalocean.com/

How to contribute to this project

Here are 3 quick ways to contribute to this project:

Make a pull request and wait for it to be merged!

Getting started

  • Fork this repository (Click the Fork button in the top right of this page, click your Profile Image)
  • Clone your fork down to your local machine
git clone https://github.com/your-username/Eco_GRiD_H23.git
  • Create a branch
git checkout -b branch-name\feature
  • Make your changes (choose from any task below)
  • Commit and push
git add .
git commit -m 'Commit message'
git push origin branch-name\feature
  • Create a new pull request from your forked repository (Click the New Pull Request button located at the top of your repo)
  • Wait for your PR review and merge approval!
  • Star this repository if you had fun!

Add your name

Add your name to the CONTRIBUTORS.md file using the below convention:

#### Name: [Neeraj Choubisa](https://github.com/Kali-Decoder/)

- Place: Udaipur, Rajasthan,India
- Designation: Full Stack Developer
- GitHub: [Kali-Decoder](https://github.com/Kali-Decoder/)
- Year : 4th
- College: IIT Roorkee

eco_grid_h23's People

Contributors

abchoudhary2512 avatar aditya007datta avatar ashishlal2003 avatar itsdivya1309 avatar jalaj028 avatar jatingupta9120 avatar kali-decoder avatar kshitizsachan avatar manindhra1412 avatar meet-javiya0 avatar mrbhatt2348 avatar nancy-30 avatar nicol4sdev avatar nikhil12377 avatar roshan23r avatar sidhuavneet avatar

Stargazers

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Watchers

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eco_grid_h23's Issues

Implement machine learning-based renewable energy forecasting

Predicting the output of renewable energy sources like solar and wind is challenging due to their variability. Machine learning models can analyze real-time data from weather forecasts, location and other factors to predict renewable energy generation, enabling better integration into the grid.
Example Refer Link - https://towardsdatascience.com/predicting-solar-power-output-using-machine-learning-techniques-56e7959acb1f

Note - Go with any dummy data which could be used to predict output.

Submit it in the ml-part folder as yourname_forecast.py file with dummy data yourname_data.csv used.

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