Heat Seekers

Inspiration

We brainstormed as a group a bunch of different ideas based on the tracks we were given. We chose sustainability because solar energy depends heavily on weather conditions like radiation, cloud cover, and temperature. We were inspired by the need for smarter, faster ways to monitor and predict solar production potential in real-time using live data and machine learning to make better energy decisions.

What it does

Our project is a real-time solar insight dashboard that collects live weather data, predicts solar energy conditions using a trained machine learning model, and visualizes insights through dynamic maps and charts. Users can monitor environmental factors and prediction traits through an interactive web platform.

How we built it

We built a full-stack system using fast API (Python) for the backend, chart.js and leaflet.js for dynamic frontend visualization, and a random forest classified model trained on live collected weather data. Weather data is pulled from OpenWeatherMap and Open-Meteo APIs, logged continuously, and used for live machine learning inference and visualization.

Challenges we ran into

We had to contribute real-time data ingestion, ensure stable API connections, fix chart updating without overloading the browser, and manage live retraining and prediction with minimal latency. It was challenging to design a system that both looked clean and handled real-time environmental data correctly.

We also ran into some challenges in the website backend process making sure the server.py file was able to process and publish data to the website. We also needed to make sure that App.js was able to send data correctly.

Accomplishments that we're proud of

we built a a fully working end-to-end system that collects real-world data, trains a machine learning model live, and visualizes intelligent predictions through an accessible web platform. the seamless integration of live weather APIs, machine learning, and dynamic visualizations was a huge achievement for the team.

What we learned

We were able to learn various topic of Full stack development, Machine Learning, Data analytics + visualization. But we also learned more about our targeted topic so about what decision making future stakeholders would make with the data we are currently working with

What's next for Heat Seekers

We plan to expand the dashboard to cover multiple cities, add historical data comparisons, retrain models dynamically based on time of year, and incorporate additional environmental factors like wind and precipitation. Our long-term goal is to create an adaptive solar energy optimization platform for broader smart energy management

Built With

• ai
• app.js
• chart.js
• gemini
• html
• javascript
• leaflet.js
• machine-learning
• open-meteo
• openweathermap
• openweathermapapi
• python
• randomforest