Authors

Iman Cumberbatch, James Wu, Linuode Ye, Godfried Junio Matahelemual, Ismail Rashad

Research Questions

• What geographic and demographic factors correlate with participation in Clean Energy Program?
• How do building traits and socioeconomic status influence energy-saving potential?
• What patterns or anomalies emerge across different customer types?

Background

As New York advances its 2050 decarbonization mandate, Consolidated Edison is expanding its clean energy programs to help customers reduce energy consumption and carbon emissions. Leveraging efficient technologies and alternative clean energy sources, the company is aligning with federal and state initiatives—particularly New York’s 2050 decarbonization mandate—to support the clean energy transition.

This Con Edison-sponsored capstone project examines how building characteristics, customer demographics, and geographic factors influence the adoption of clean energy programs across the service territory. A key focus is on establishing various participation profiles across customer and building type that would indicate a high likelihood of making impact on clean energy goals based of fit for adoption of program technologies. By analyzing participation among residential, small business, commercial, and multifamily customers—including those in disadvantaged communities (DAC) and non-DAC areas—the project aims to help Con Edison identify tailored strategies that boost engagement, increase return on investment (ROI), and encourage equitable adoption of energy-efficient practices.

Methodology

The project leverages a multi-layered dataset, including Residential, Small/Medium Business (SMB), and Multi-Family (MF) energy usage data, PLUTO building characteristics, socioeconomic indicators, and Local Law 84 benchmarks. The team assessed program participation by exploring energy trends across census tracts and identifying clusters with high or low adoption of clean energy measures such as Clean Heat and Building Envelope upgrades. Tools like Power BI, Python, and GIS are used to uncover insights, while key variables are structured into a unified data framework. This includes a metadata dictionary and a column enrichment process to support repeatable, scalable analysis.

Deliverables

Framework & Metadata


• Structured data dictionary covering building, demographic, and energy usage variables

• Column enrichment mapping (customer profiles, location analysis, program-specific variables)


Excel and Python Output


• Final dataset with enriched columns for demographic, PLUTO, and program data

• Adjustable filters for customer segmentation and geographic criteria


Power BI Dashboard


• Interactive visualizations including:

  • Customer profile insights

  • Participation and adoption by location

  • Incentive/savings impact by program name

  • Mapping visualizations to identify opportunity areas


Final Showcase & Dissemination


• Poster Presentation featuring methods, findings, and actionable recommendations

• Public-facing (masked) version through NYU CUSP Repository and Con Edison reports