Energy systems are rapidly evolving in both the developed and developing world. Distributed energy resources such as solar photovoltaics are experiencing significant growth, but are not typically controlled or monitored by independent system operators of the grid. At the same time, one in five people around the world still lack access to modern electricity, and identifying optimal pathways to electrification requires detailed knowledge of local infrastructure and the precise locations of communities that would benefit most from electrification. Here, we work to fill these important information gaps by analyzing satellite imagery and other remotely sensed data using machine learning techniques, specifically through convolutional neural networks. To demonstrate the potential of this approach, we'll discuss three case studies, each entirely based on remotely sensed data: (1) identifying the location and capacity of solar photovoltaic arrays in the United States, (2) assessing energy access in India, and (3) estimating building energy consumption in Florida. We'll also discuss the challenges to scaling these approaches across geographies and different sources of remote sensing data.
Kyle Bradbury is the Managing Director of the Energy Data Analytics Lab at the Duke University Energy Initiative where he leads applied research projects at the intersection of machine learning techniques and energy problems. His research includes developing techniques for automatically mapping global energy infrastructure and access from satellite imagery; transforming smart electric utility meter data into energy efficiency insights; and exploring the reliability and cost trade-offs of energy storage systems for integrating wind and solar power into the grid. He received both a Ph.D. in energy systems modeling and an M.S. in electrical and computer engineering from Duke University, as well as a B.S. in electrical engineering from Tufts University. He has worked for ISO New England, MIT Lincoln Laboratories, and Dominion.