Predicting building energy savings

Fellows: Scott Alfeld, Andrea Fernandez Conde, Camelia Simoiu
Mentor: Brandon Willard
Project Partner: Berkeley Lab, Agentis Energy
[Project Blog Post] [Github Repo]

Energy efficiency is supposed to be the low hanging fruit of clean energy. But few people are investing in building energy retrofits. This is because the potential energy savings vary wildly by building, so the return on investment of fixing up property is highly uncertain.

The Lawrence Berkeley National Laboratory – a scientific research facility funded by the Department of Energy – wants to use data to help businesses and homeowners understand their how much less energy their building could be using with the right modifications.

Fellows will analyze energy data from Agentis Energy on thousands of buildings across the United States, using Berkeley Lab’s building fingerprint tool to predict future energy savings for different kinds of buildings. The goal is to make it possible for private investors to fund energy efficiency projects at scale.