GridPulse AI

Inspiration

Buildings consume a major share of electricity, but identifying where clean-energy upgrades would have the greatest impact is often difficult, expensive, and time-consuming. We wanted to create a tool that makes sustainability data easier to understand by turning building footprints, climate conditions, and incentive information into a visual, location-based experience.

What it does

GridPulse AI is a Florida building-footprint and clean-energy screening application. Users can explore buildings on an interactive map and view estimated clean-energy opportunities based on building characteristics and local climate data.

The application combines OpenStreetMap building footprints, Open-Meteo climate records, public incentive references, and Gemini-generated summaries. It helps users quickly evaluate potential solar-energy opportunities, estimated environmental impact, possible cost savings, and relevant clean-energy incentives.

GridPulse AI is designed as an early screening tool rather than a replacement for a professional energy audit.

How we built it

We built GridPulse AI as a React-based web application with an interactive map interface. OpenStreetMap provides building-footprint and geographic data, while Open-Meteo supplies historical and location-specific climate information.

We created calculations that translate building and weather data into understandable clean-energy estimates. Gemini is then used to generate concise summaries explaining the building’s energy potential, expected benefits, and possible next steps.

Because the project was developed during a 10-hour hackathon, we focused on building a functional end-to-end prototype that could collect data, analyze a selected building, and present the results through a simple visual interface.

Challenges we ran into

One of our biggest challenges was combining several data sources that used different formats, geographic structures, and levels of detail. Building footprints do not always contain complete information, so we had to design reasonable fallback assumptions without presenting the results as exact engineering calculations.

We also encountered API authentication, model configuration, and frontend integration issues while connecting Gemini to the application. Another challenge was balancing technical detail with usability so that the results would remain useful to both technical and nontechnical users.

Accomplishments that we're proud of

We are proud that we created a working clean-energy screening platform within a limited hackathon timeframe. GridPulse AI brings together mapping, climate data, sustainability calculations, public incentives, and generative AI in one application.

We are especially proud of the interactive building-selection experience and the way Gemini converts complex calculations into summaries that are easier for users to understand. Instead of only displaying raw numbers, the platform explains what the estimates could mean for a specific building.

What we learned

We learned how important data quality and transparency are when building sustainability tools. Even when exact building-energy data is unavailable, public datasets can still support useful early-stage estimates as long as assumptions and limitations are clearly communicated.

We also gained experience integrating geographic data, external APIs, React components, and generative AI into a single workflow. Most importantly, we learned how to prioritize essential features and rapidly debug a full-stack application under a strict time constraint.

What's next for GridPulse AI

Our next step is to improve the accuracy of the platform by incorporating verified building-energy consumption, roof geometry, electricity rates, utility service territories, and more detailed solar-production models.

We also want to expand beyond screening individual buildings by allowing users to compare multiple properties, identify high-impact areas across a city, and generate downloadable sustainability reports. Future versions could include rooftop suitability analysis, battery-storage recommendations, wind-energy screening, financial payback calculations, and connections to verified federal, state, and local incentive databases.

Ultimately, GridPulse AI could become a planning tool for property owners, universities, businesses, utilities, and local governments seeking to prioritize clean-energy investments.