Predicting Building Energy Savings
Fellows: Scott Alfeld, Andrea Fernandez Conde, Camelia Simoiu
Data Science Mentor(s): Brandon Willard
Project Partner: Berkeley Lab, Agentis Energy
Energy efficiency is supposed to be the low hanging fruit of clean energy. In 2013, few people were investing in building energy retrofits because the potential energy savings varied wildly by building, so the return on investment of fixing up property was highly uncertain.
The Lawrence Berkeley National Laboratory – a scientific research facility funded by the Department of Energy – wanted to use data to help businesses and homeowners understand their how much less energy their building could be using with the right modifications.
In 2013, DSSG fellows analyzed 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 was to make it possible for private investors to fund energy efficiency projects at scale.
You can read more about this project by visiting our github repository.