Harnessing the power of the sun should be as intuitive as glancing at a map, but with precision tailored to your unique housing setup. SunlightSavings is a sophisticated yet user-friendly tool that empowers users to make data-driven decisions about maximizing solar energy production and financial savings.

Many solar optimizers today offer cookie-cutter recommendations, failing to maximize energy savings or optimize designs for unique housing setups. SunlightSavings dares to rethink simplicity and depth in renewable energy solutions.

Designed for property owners in North Carolina, SunlightSavings provides more than a generic layout of sun-kissed rooftops—it serves as a personalized advisor, analyzing solar incidence with precision 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 users step-by-step toward achieving them.

SunlightSavings is a powerful web-based solar optimization tool that:

• Pinpoints the sunniest areas of any building using precise sunlight mapping and advanced roof detection.
• Optimizes panel placement for maximum energy production, factoring in roof type, solar incidence, and shading.
• Supports multiple commercial solar panel models to ensure tailored recommendations based on real efficiency differences.
• Conducts a detailed cost-benefit analysis, projecting:
  • Energy output
  • Installation costs
  • Long-term savings
  • Tax incentives and credits
• Uses the proprietary Kumar-Mendoza Equation to calculate Net Present Value (NPV), factoring in:
  • Energy production
  • Initial implementation costs
  • Depreciation values
  • NC energy rates

With SunlightSavings, users can maximize their savings, reduce their carbon footprint, and make informed solar investments.

Frontend:

• React.js for a smooth and interactive user experience.
• Google Maps API for rooftop visualization and user-friendly navigation.

Backend:

• Flask for efficient data processing and API handling.
• Google Solar API to retrieve solar incidence, elevation data, and map layers.
• NumPy & GeoTIFF encoders to process and generate precise heatmaps.
• Google Geocoding API to convert user-provided addresses into geographic coordinates.

Financial & Solar Calculations:

• Python’s scientific computing libraries for:
  • Solar efficiency modeling
  • Cost-benefit analysis
  • Custom NPV equation (Kumar-Mendoza Equation)

• Most APIs lacked the accuracy required for our analysis.
• The Google Solar API provided a strong foundation, but limited documentation meant extensive experimentation and debugging.

• Needed to align and merge multiple geographic datasets.
• Used GeoTIFF encoders with NumPy to generate accurate solar radiation heatmaps.

• Balancing solar panel efficiency, cost projections, and energy output required extensive financial modeling.
• The final equation provides users with actionable, data-driven insights.

• Handling GeoTIFF requests required efficient workflows and asynchronous processing.

✅ A fully functional, user-friendly solar optimizer.
✅ Successfully integrating the Google Solar API despite limited documentation.
✅ Developing the Kumar-Mendoza Equation, a sophisticated custom NPV model for real-world solar investments.
✅ Bridging complex geographic and financial calculations into an intuitive, interactive interface.
✅ Collaborating as a team for the first time and solving technical challenges in under 24 hours!

🔹 API Integration Best Practices:

• Working with a new API requires trial, error, and persistence.
  🔹 Efficient Data Processing for Geographic Analysis:
• Merging multiple GeoTIFF layers using NumPy.
  🔹 Building a Scalable Financial Model:
• Crafting the Kumar-Mendoza Equation for solar ROI analysis.
  🔹 Asynchronous Workflows in Web Development:
• Handling time-sensitive API requests and ensuring fast, smooth UX.
  🔹 The Power of Teamwork Under Pressure:
• Collaboration, adaptability, and problem-solving led to a finished product in record time! 🚀

🔜 Expanding Nationwide

• Adapting our models for state-specific incentives, energy rates, and carbon offsets.

🔜 Localized Financial and Regulatory Data

• Integrating tax credits, rebate programs, and net metering policies state-by-state.

🔜 Incorporating More Solar Panel Models

• Providing deeper customization for panel selection.

• React.js
• Flask
• Google Maps API
• Google Solar API
• Google Geocoding API
• NumPy
• GeoTIFF Encoders
• Python (Pandas, NumPy, SciPy)
• Financial Modeling (Kumar-Mendoza Equation)

🙌 Proudly built at HackDuke 2025! 🚀