| Vaayuputras is a website built using Django framework and It aims to provide a global solution for Wind Power Prediction for any wind farm on earth. |
Website Hosted on IBM Cloud Foundry (Currently Website is down due to the expiry of free tier resources.)
Here is the working live website : https://vaayuputras.eu-gb.cf.appdomain.cloud/
Youtube Link : https://www.youtube.com/watch?v=NY4ssdP0uEY&feature=youtu.be
- Wind Power Prediction for any wind farm on earth
- Select wind farm from database
- Select wind farm directly from Google Maps
- Wind Farm Economics Calculator
- Power given to user to Train their own model with their owned wind farm data and use it for prediction.
- RESTful API Support for Wind Power prediction and Wind Farm Economics calculator for both Auth and Non-Auth Users.
- Very Powerful IBM Watson Assistant which can perform Wind Power prediction, Do Wind Farm Economics and Chat with Users about Wind Farm Terminologies.
- Login and Sign Up System with Integrated Google, Facebook and Twitter Sign-In.
The Site is packed with numerous services with beautiful UI for complete User experience.
- Django
- Tensorflow
- Keras
- Material Kit 2.0 UI
- Rest Framework for Django
- Watson Machine Learning
- Watson Assistant
- IBM Elephant SQL (Postgre SQL) Service (for Database Management)
- IBM Cloud Foundry (for Deployment)
- Docker Containerization
- Google Cloud Maps API
- Javascript libraries like Jquery, Popper, NoUISlider, Stats, Plotly etc.
- Weather APIs from OpenWeatherMaps and Climacell.
All the Data which was gathered from numerous sources and websites is available HERE on Google Drive.
A Kaggle Notebook which was made by Me for Kaggle Dataset Analysis and also received Silver Medal is Available HERE
Other Notebook which contains All the Data Cleaning, Analysis, Machine Learning Experiment and Deep Learning Modelling using LSTM is available in Repo itself.
- Fork the Repository
- Create a Django Environment for vaayuputras_source_code
- Install dependencies from Pipfile.lock
- Connect your database and migrate app
- Create IBM Cloud Account and Deploy the model located in media folder
- Replace all the credentials and API keys with your own
- You are all set to Go...
- Follow above all the instructions
- Create Cloud Foundry Service for Python Django App from IBM Cloud
- Push All the Source Files to Gitlab repository on IBM Cloud
- You have completed deploying.
| Chittal Patel | Dhruvil Dholariya |
|---|---|
| [email protected] | [email protected] |
If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result.
If you'd like to request a new function, feel free to do so by opening an issue here. Please include sample queries and their corresponding results.
Want to contribute? Great!
To fix a bug or enhance an existing module, follow these steps:
- Fork the repo
- Create a new branch (
git checkout -b improve-feature) - Make the appropriate changes in the files
- Add changes to reflect the changes made
- Commit your changes (
git commit -am 'Improve feature') - Push to the branch (
git push origin improve-feature) - Create a Pull Request