AI is CMU’s Secret Weapon for Greener Buildings

By: Azadeh Sawyer

CMU researchers are using AI to predict building energy use and emissions — at daily, weekly, and yearly intervals.

Why it matters: Buildings account for nearly 40% of global carbon emissions, but the burden isn’t shared equally. In many U.S. cities, lower-income communities pay up to 20% more for energy than higher-income areas because their homes are older and less efficient.

Catch up quick: Many buildings, especially older ones, struggle with energy efficiency. Poor insulation, outdated HVAC systems, leaky windows, and inefficient lighting all contribute to higher energy use and emissions — leading to higher bills and environmental impact.

Key insight: The CMU project investigates how AI can help make energy benchmarking — comparing a building’s energy use and emissions with peers — a more accessible tool for sustainability. Currently, less than 50 cities in the U.S. deploy energy benchmarking to reduce emissions, and just a fraction of them make annual benchmarking data available to the public.

What we’re doing: With support from a Scott Institute seed grant, our AI model analyzes energy and emissions data across multiple time scales, offering a more detailed, scalable, and equitable approach to benchmarking. This will create opportunities for cities without formal programs to better understand building performance, prioritize retrofits, and reduce emissions.

For example, if one building uses a lot more energy compared with a peer group, then end users such as engineers, utilities, and building owners can respond appropriately.

What’s next: The model has several implications for future uses, including:

  • Delivering actionable benchmarking for entire neighborhoods or cities, even in the absence of energy disclosure laws, with minimal data inputs such as utility bills and public records.
  • Reducing the need for costly, labor-intensive energy audits while still delivering performance assessment.
  • Integrating utility usage, weather data, and building characteristics to train algorithms to estimate energy use and flag anomalies across a city's building stock.
  • Transferring models that can adapt to local communities with limited data.
  • Directing funding toward the buildings most in need of efficiency upgrades.