The volume of data researchers can collect on how buildings use energy has changed dramatically in recent years, and that is transforming approaches to energy efficiency. Mary Ann Piette, director of the Building Technology and Urban Systems division at the U.S. Department of Energy's Lawrence Berkeley National Lab (Berkeley Lab), discusses this shift and other trends in building energy efficiency in a recent episode of the podcast Global Minima.
"We're able to collect data at scales that we weren't able to do 10 years ago," said Piette, adding that this bodes well for methods like model predictive control, where different factors in a building's energy consumption are controlled as a system with the help of machine learning and artificial intelligence.
Today, a building's energy data can be transferred electronically. That's a far cry from Piette's early days at the Lab in the 1980s, when she would call up building owners to collect utility bills from before and after retrofits, manually entering the information into a computer database.
Berkeley Lab's current database has energy use data for over a million commercial and residential buildings, Piette said, and allows a researcher to query and understand how buildings perform compared to others. The field is moving away from focusing just on how much energy a building uses to looking at when that use happens.
"Energy efficiency is often getting the most services out of every kilowatt hour," Piette said. "We still care a lot about that, but we're also doing more work on the electric load shape of a building and how to make that load shape flexible so that we can use more energy when the renewables are more available and less when they're not."