A Wider Lens on Energy: Adapting Deep Learning Techniques to Inform Energy Access Decisions (2019-2020)
One in nine people do not have regular access to electricity, which can have negative impacts on health, educational and economic opportunities, and quality of life. In order to determine the optimal strategy for meeting the needs of communities without access, one must first identify where electricity infrastructure already exists.
This information can help developing countries decide whether to expand the national grid, construct microgrids or provide off-grid solar power. However, current approaches to identifying and mapping energy infrastructure are expensive and time intensive.
Building off the work of the 2018-2019 project team and a Data+ summer project, this team investigated improvements to deep learning models that identify energy infrastructure in satellite imagery. A key adjustment involved creating and integrating synthetic data (a new approach to this problem) into the AI model training process. The end goal is to generate maps of power grid networks that can aid policymakers in implementing effective electrification strategies.
Fall 2019 – Spring 2020
Kyle Bradbury. Estimating Solar PV Locations and Capacity in San Diego, CA ($14,971 grant awarded from the Department of Energy, 2019)
Kyle Bradbury. Multi-task Learning for Solar Photovoltaic Army Detection ($30,999 grant awarded from the Department of Energy, 2019)
Kyle Bradbury, Jordan Malof, Brian Murray, Convergence Accelerator Phase I (RAISE): Open Knowledge Network for the Global Energy Data Commons ($974,140 grant awarded from the National Science Foundation, 2019)
Mapping Worldwide Energy Infrastructure (Fortin Foundation Bass Connections Virtual Showcase 2020)
This Team in the News
See related teams, Deep Learning for Rare Energy Infrastructure in Satellite Imagery (2020-2021) and Energy Data Analytics Lab: Energy Infrastructure Map of the World through Satellite Data (2018-2019), and Data+ summer project, Deep Learning for Rare Energy Infrastructure in Satelittle Imagery (2020).
- Kyle Bradbury, Pratt School of Engineering-Electrical & Computer Engineering|Energy Initiative
- Leslie Collins, Pratt School of Engineering-Electrical & Computer Engineering
- Jordan Malof, Pratt School of Engineering-Electrical & Computer Engineering
/graduate Team Members
Vivek Sahukar, Interdisciplinary Data Science - Masters
/undergraduate Team Members
Ayooluwa Balogun, Mechanical Engineering (BSE)
Scott Heng On, Computer Science (BS), Statistical Science (BS2)
Gaurav Uppal, Mechanical Engineering (BSE)
Jason Wang, Computer Science (BS)
/yfaculty/staff Team Members
T. Robert Fetter, Nicholas Institute for Environmental Policy Solutions
Marc Jeuland, Sanford School of Public Policy
Luana Lima, Nicholas School of the Environment-Environmental Sciences and Policy
Robyn Meeks, Sanford School of Public Policy