RenewWeb

Inspiration

We are inspired by how renewables increasingly look like our only option in the future, so what we wanted to do was to create a website where energy companies can easily look for the optimal locations to set up new renewable energy facilities!

What it does

Our website can find the optimal locations for all renewable resources, as well as providing the energy output and profit for each location so the company knows how much money they can gain from setting up their facilities at that location.

How we built it

We built ML models to find the best locations for renewable resources, then displayed our findings on Streamlit. ML and Streamlit both use the Python language. We needed the help of ChatGPT and Claude to help us find bugs in our website.

Challenges we ran into

One of the challenges was trying to access our ML answers to then display on the website. We were unable to find many datasets for geothermal and hydropower, and therefore were unable to create an optimal model for the two. It was also our first time using a Python framework for web development, so we had many issues with the setup.

Accomplishments that we're proud of

We were able to build a machine learning model, then display our findings on the website and able to leverage api for good cause

What we learned

We learned a lot about Streamlit since we all had little to no experience in the Python framework, and we all learned interesting facts about nauced information about the best locations and conditions to set up renewable energy facilities and the challenge of finding a dataset that is useful

What's next for RenewWeb

Our next steps is to then continuously improve our website so that it contains the latest data about weather and geological factors, improve our ML models so that our website is able to help companies find even better places to set up renewable facilities.

Built With

• chatgpt
• claude
• cursorai
• nrelapi
• openaiapi
• openstreetmap
• python
• sklearn
• streamlit