Community Solar Equity Mapping Project

ISEF Category: Energy: Sustainable Materials and Design

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: Full Year

The Hook

A solar panel on a roof can do more than cut one family's bill. It can shift who gets clean power, who saves money, and who gets left out. Your project can ask where solar would help the most, not just where it fits. That makes this a strong mix of energy, equity, and data science.

What Is It?

This project studies where community solar should go if you care about both energy output and fairness. Community solar lets many households share one solar array instead of putting panels on each roof. That matters for renters, shaded homes, and families that cannot afford rooftop systems.

Think of it like placing water stations in a hot city. You would not place them only where rich people already have backyard space. You would look at need, access, and reach. Here, you compare rooftop potential, local electricity costs, and census data to find places where solar could help the most people.

You can turn that idea into a map and a ranking system. You may score neighborhoods by solar suitability, energy burden, or access barriers. Then you test whether your model favors efficiency only, or balances efficiency with equity.

Why This Is a Good Topic

This is a strong science fair topic because you can test real datasets and build a clear scoring model. You connect clean energy to a real problem, high utility bills and uneven access to solar. You can learn data cleaning, mapping, weighting variables, and basic statistics without a wet lab. That gives you room to do original work, not just a summary report.

Research Questions

• How does adding census income data change which neighborhoods rank highest for community solar placement?
• What is the effect of using electricity-cost data on the final solar priority map?
• Does weighting rooftop suitability more than equity measures change the top ranked sites?
• To what extent do high-burden neighborhoods overlap with high solar potential areas?
• Which combination of rooftop potential, electricity cost, and census variables best predicts solar benefit?
• How does a fairness rule change the number of low-income neighborhoods selected for solar siting?

Basic Materials

• Laptop or desktop computer with internet access.
• Spreadsheet software such as Google Sheets or Excel.
• Free mapping tool such as Google My Maps or QGIS.
• Open census data from the U.S. Census Bureau.
• Rooftop solar potential data from Google Project Sunroof, if available for your area.
• Local electricity rate data from your utility or state energy office.
• Notes template for tracking variable definitions and source dates.

Advanced Materials

• Laptop or desktop computer with internet access.
• QGIS for spatial analysis and map layering.
• Python with pandas, geopandas, and matplotlib for cleaning and scoring data.
• Census tract shapefiles from the U.S. Census Bureau TIGER/Line files.
• Solar irradiance or climate data from NASA or NOAA.
• Public utility rate datasets or state energy commission data.
• Statistical software or scripts for sensitivity testing and correlation checks.

Software & Tools

• QGIS: Maps census tracts, rooftop potential, and priority scores in one spatial workflow.
• Google Sheets: Organizes datasets, calculates weighted scores, and compares ranking methods.
• Python: Cleans large datasets and tests how different scoring rules change results.
• ImageJ: Not usually needed for this project, but useful if you later analyze map screenshots or exported figures.
• PubMed: Helps you find review articles on energy burden, solar access, and environmental justice.

Experiment Steps

1. Define the exact decision you want to improve, such as finding where community solar would lower bills for the most households.
2. Choose a geographic unit, such as census tracts or ZIP codes, so every dataset lines up on the same map.
3. Collect rooftop potential, census, and electricity-cost data, then check that each source covers the same area and time frame.
4. Build a scoring method that combines solar potential with equity measures, then justify each weight in writing.
5. Test how your rankings change when you change the weights or remove one dataset, so you can see which variables matter most.
6. Present your results as maps, ranked lists, and a short policy recommendation that explains the trade-offs clearly.

Common Pitfalls

• Mixing data at different geographic scales, which makes tract-level comparisons unfair or impossible.
• Treating rooftop solar potential as the same as community solar need, which ignores renters and shaded homes.
• Using outdated electricity rates, which can distort the energy-burden calculation.
• Choosing weights without explaining them, which makes the ranking look arbitrary.
• Making a map that looks polished but does not test whether the result changes when you tweak the model.

What Makes This Competitive

A strong version of this project does more than rank neighborhoods. It explains why the ranking changes when you change assumptions, and it tests whether the model stays stable. You can stand out by building a fairness metric, comparing several weighting schemes, or checking your results against local policy data. Clean maps help, but the real strength comes from the logic behind the map.

Project Variations

• Focus on one city and compare solar equity across neighborhoods with different income levels.
• Replace electricity cost with energy burden data to see which areas spend the largest share of income on power.
• Compare rooftop solar potential with community solar access in rural, suburban, and urban census tracts.

Learn More

• U.S. Census Bureau: Search ACS tables for income, housing, and rent data for your study area.
• NOAA Climate Data Online: Find sunlight, cloud cover, and climate context for your map.
• NASA Earthdata: Search for irradiance and land surface datasets that support solar potential analysis.
• U.S. Energy Information Administration: Look up electricity price and household energy data by state and region.
• MIT OpenCourseWare: Search for free courses in data analysis, statistics, and urban planning to support your methods.
• Energy Research and Social Science: Search the journal for articles on energy equity, energy burden, and solar access.