🏠 Residential Energy Use Analysis (RECS 2009)

This project analyzes U.S. household energy consumption patterns based on the 2009 Residential Energy Consumption Survey (RECS) dataset. It focuses on how climate zones and housing types influence space heating and cooling energy use across U.S. regions.

---

📊 Key Insights

• Northeast & Midwest: Highest space heating loads (40–60% of total energy).
• South: Significant cooling demand (10–20%), especially in mobile homes.
• Housing Type Matters: Detached homes consume far more than apartments.

---

📁 Files Included

File Name	Description
Residential_Energy_Use_Analysis.ipynb	Jupyter notebook with full analysis and visualizations
heating_energy_share_by_region.png	Chart: Heating energy % by region and housing type
cooling_energy_share_by_region.png	Chart: Cooling energy % by region and housing type
Residential_Energy_Use_Portfolio.pdf	Exported PDF report with narrative and visuals

---

🧰 Tools & Libraries

• Python 3.10+
• pandas for data wrangling
• matplotlib for plotting
• fpdf for PDF generation

---

📦 Dataset

• Source: U.S. EIA RECS 2009 Public Use File

---

🧠 How to Use

1. Open the notebook:
   You can use Jupyter, VS Code, or Google Colab.

2. Run the analysis:
   Explore how energy use shifts with geography and building characteristics.

3. Customize it:
   Extend with new datasets (e.g., 2015, 2020 RECS), or add factors like income, square footage, or occupancy.

---

🌱 Next Steps

• Add a machine learning model to predict total energy use
• Compare RECS 2009 with more recent years
• Simulate climate change impact on regional energy demands

---

🧑‍💻 Author

[Your Name]
Energy Analyst Portfolio • [LinkedIn] | [Email]