Researchers at Duke’s Energy Data Analytics Lab have been pioneering the use of automated object identification and machine learning techniques to assess and map distributed energy generation, based on high-quality aerial imagery like that provided by satellites and drones.
Researchers began by addressing a critical information gap regarding rooftop solar photovoltaic (PV) capacity and power generation: government statistical collections have focused on central station power, so information about distributed energy production at residential and commercial buildings is scarce.
Project members first compiled, curated, and published a ground-truth data set, then trained image recognition algorithms to estimate the size and location of solar panels. Their methods can be used to improve solar PV estimates and aid government agencies and power grid independent system operators (ISOs) in evaluating the state of distributed PV deployment and use that information for planning purposes to increase system reliability and resilience.
One of the project’s long-term objectives is to create a map of global energy infrastructure that can be automatically updated, and work is well underway on this front.
