Solar Energy Analytics
A four-part dashboard suite exploring how residential solar panel systems perform and pay off over time. Built entirely from my own home’s solar and utility data.
- 💸 Cost Analysis: Tracked utility bills, solar credits, and monthly savings from net metering across 12+ months of data and projected into the future.
- 🔄 What‑If Scenario: Modeled electricity rate changes and their impact on long-term payback using adjustable price inputs.
- 📈 Production vs. Consumption: Visualized daily solar generation vs. household usage and grid exchange, surfacing seasonal patterns.
- 📊 Prediction Evaluation: Assessed forecasting accuracy for modeled Random Forest solar generation against actuals.
Tech Stack: Python, Pandas, Matplotlib, Plotly, Scikit-learn, GitHub, Tableau (frontend).
Impact: Helped optimize energy usage habits, catch panel underperformance early, and estimate ROI under changing market conditions.
This wasn’t just an analytics project — it was a way to turn my own household into a living lab. It sharpened my modeling skills, deepened my MLOps fluency, and reminded me why applied data science matters.