CX-102100: Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Award Number: DE-EE0009359, CX(s) Applied: A9, Solar Energy Technologies Office, Location(s): CA, Office(s): Golden Field Office

Office of NEPA Policy and Compliance

March 23, 2021
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Award Number: DE-EE0009359
CX(s) Applied: A9
Solar Energy Technologies Office
Location(s): CA
Office(s): Golden Field Office

The U.S. Department of Energy (DOE) is proposing to provide funding to Stanford University to develop and train machine learning modules that use raw imagery data (satellite imagery, street views, road networks etc.) to create desired mappings of solar energy resources and infrastructures.