SunlightSavings

• 

  Page 1

• 

  Page 2

Inspiration

What if harnessing the power of the sun could be as intuitive as glancing at a map, but with precision tailored to your unique housing setup?

SunlightSavings dares to rethink simplicity and depth in renewable energy solutions. Many solar optimizers today offer cookie-cutter recommendations, failing to maximize energy savings or optimize designs for unique housing setups. Designed for the property owners in North Carolina, it offers more than a generic layout of sun-kissed rooftops-- it serves as a personalized advisor, analyzing solar incidence with finesse while accommodating a range of solar panel models that most competitor optimizers overlook.

With its blend of user-oriented features and robust performance metrics, this optimizer not only identifies solar efficiency opportunities but also guides you step-by-step toward achieving them!

What it does

SunlightSavings is a sophisticated yet user-friendly tool that empowers users to make data-driven decisions about maximizing solar energy production.

By combining precise sunlight mapping and advanced roof detection, it identifies the sunniest areas of any building, pinpointing optimal locations for solar panel installation. Unlike other tools, SunlightSavings supports a wide range of commercial solar panel models and roof types, tailoring recommendations to each user's unique setup while accounting for efficiency differences.

Beyond panel placement, the optimizer conducts a detailed cost-benefit analysis. It projects energy output, installation costs, and long-term savings, providing users with a clear financial roadmap. Using the proprietary Kumar-Mendoza Equation (developed by members of our team), it calculates the net present value (NPV) by analyzing potential energy production, initial implementation costs, depreciation values, and NC energy rates.

This seamless blend of technical precision and financial insight turns the solar installation process into a strategic, data-backed decision. With SunlightSavings, users can maximize their energy savings, reduce their carbon footprint, and take a meaningful step toward a sustainable future.

How we built it

We developed the user-facing interface with React.js to ensure a smooth and interactive experience. On the backend, the web application is powered by Flask, providing efficient data handling and communication.

To visualize rooftops and geographic data, we integrated the Google Maps API, while the Google Solar API supplied essential solar incidence, elevation data, and map layers. These layers were processed and merged using NumPy and GeoTIFF encoders to generate precise heatmaps. To make the tool user-friendly, the Google Geocoding API converts user-provided addresses into accurate geographic coordinates for location-based analysis.

The backend handles complex calculations, including solar efficiency, cost-benefit analysis, map data layering, and the proprietary Kumar-Mendoza Equation. These were implemented using Python’s scientific computing libraries, ensuring accurate and reliable results.

Challenges we ran into

One of the key challenges we faced was identifying an API capable of delivering precise solar incidence data. Many existing APIs lacked the necessary accuracy to integrate seamlessly with various solar panel models. After selecting the Google Solar API as a suitable candidate, we encountered another hurdle: working with a relatively new API that offered limited documentation and sparse online support. This required extensive experimentation and debugging to understand its features and integrate it effectively.

Layering map data added another layer of complexity. We needed to process and align multiple data layers using NumPy and GeoTIFF encoders, ensuring the data was mapped and displayed correctly. This process demanded precision and thorough testing to achieve reliable results.

Developing the Kumar-Mendoza Equation for calculating solar NPV also proved challenging. It required balancing factors like solar panel efficiency, cost projections, and energy output to deliver accurate and actionable financial insights for users.

Finally, merging the web app’s frontend with the Python backend introduced its own set of difficulties, particularly when handling GeoTIFF requests from the API. Ensuring smooth communication between the two systems took significant effort, involving debugging and optimizing the data flow.

Accomplishments that we're proud of

One of our proudest accomplishments is the successful development of a fully functional, user-oriented Solar Panel Optimizer that bridges complex data processing with a simple and intuitive interface. We are especially proud of successfully integrating the Google Solar API despite its limited documentation, overcoming challenges through experimentation, persistence, and creative problem-solving. The Kumar-Mendoza Equation is another milestone, as we crafted a sophisticated, custom NPV model that offers users actionable insights tailored to different solar panel models and installation configurations.

What stands out most is how we came together as a team, working collaboratively for the first time and managed to tackle all sorts of technical challenges, from aligning GeoTIFF data layers to implementing user friendly cost-benefit analysis.

This project has shown us that even the most complex problems can be solved when diverse talents, a strong will, patience, and 24-hours are combined!

What we learned

We learned not just technical skills but also the importance of adaptability, collaboration, and perseverance when facing unexpected challenges. On the technical side, we gained a deeper understanding of integrating APIs efficiently, particularly when dealing with a relatively new API. We sharpened our skills in data processing and merging multiple map layers, using tools that worked with new data types we were previously unfamiliar with to handle complex spatial datasets and ensure accurate visualization. Developing the Kumar-Mendoza Equation for ROI taught us how to balance mathematical precision with user-centered insights, as we needed to ensure the equation was both practical and scalable for different panel models and configurations. Also, working with time-sensitive requests, like the GeoTIFF data, emphasized the importance of efficient workflows, debugging, and handling asynchronous tasks effectively.

Perhaps the most valuable lesson was the power of teamwork and effective problem-solving under pressure. This project demonstrated how different perspectives and collaborative brainstorming could overcome obstacles faster and more creatively than individual effort alone.

What's next for SunlightSavings

SunlightSavings is looking to expand nationwide by adapting our models to account for state-specific tax incentives, energy rates, and carbon emissions. Our next step is to integrate localized financial and regulatory data, ensuring that homeowners across the U.S. receive tailored solar recommendations. By leveraging additional data sources alongside the Google Solar API, we aim to provide accurate, region-specific analyses that maximize savings and environmental impact.

Built With

• google-maps
• numpy
• python
• react.js