Overview

Personal Profile

Daniel Bernal works on integrating onsite energy generation, conversion and storage to supply electrical and thermal loads to maximize cost savings using REopt and SAM. Bernal's skills also include techno-economic analysis of novel technology scale-ups. Bernal's projects span from industrial sites to urban and rural microgrids within the U.S. and abroad.

Research Interests

Industrial systems and energy

Workforce development

Optimization methods

Machine Learning

Circular economics

Education/Academic Qualification

Bachelor, Sustainability Science, Furman University

Master, Industrial Engineering, University of Texas at El Paso

Certificate, Machine Learning for Graph Networks

Certificate, Python Beginner Student

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Collaborations and Top Research Areas From the Past 5 Years

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