Award Number: DE-EE0008765, CX(s) Applied: A9, Geothermal Technologies Office, Location(s): CA, Office(s): Golden Field Office
Office of NEPA Policy and Compliance
May 30, 2019
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Award Number: DE-EE0008765
CX(s) Applied: A9
Geothermal Technologies Office
Location(s): CA
Office(s): Golden Field Office
The U.S. Department of Energy (DOE) is proposing to provide federal funding to the University of Southern California (USC) for the design, development, and implementation of machine learning techniques for the efficient management, optimization, and automation of geothermal energy recovery operations. USC would develop physics-informed predictive analytics tools by integrating varied multimodal datasets. Predictive models would be used in model predictive controllers for optimization and automation of geothermal operations. Additionally, machine learning methods would be used to forecast future operating conditions and fault diagnosis for risk assessment and mitigation. Geothermal field data collected by a private industry partner, separate from the DOE project, would be used to test the developed algorithms. Project activities would consist exclusively of literature review, data analysis, computer modeling and simulation that would take place at USC in Los Angeles.