Scott Vitter

  • Graduate Research Assistant, The University of Texas at Austin
  • Captain, Texas Army National Guard

After studying mechanical engineering at the University of Notre Dame, Scott joined the US Army as an Engineer Officer.  Scott worked in a variety of roles during four years of active service, including platoon leader, executive officer, and project manager.  During a deployment to southwest Afghanistan in 2013, Scott observed how energy and water scarcity created both challenges and opportunities for the fledgling Afghan democracy.  Upon leaving active service, and having already caught the “energy bug,” Scott landed at the Webber Energy Group to study energy and water linkages in urban settings.

Some of his military education includes Ranger School, Airborne School, Pathfinder School, and Jumpmaster School.  He is a Senior Rated Jumpmaster, and is currently concluding a stint in the Texas Army National Guard. Other than research, his favorite hobbies are squash, spikeball, podcasts, endurance running, kombucha brewing, and beer tasting.

Research Topics

Scott’s work in the energy-water nexus focuses on the linkages between urban energy and water systems.  Treatment, distribution, and sanitation services for water consume large amounts of electricity.  Additionally, energy and water are often consumed simultaneously in the built environment to run appliances, especially ones using hot water.  Because these systems are linked, decisions and policies impacting urban water systems also influence the electricity system, and vice versa.  At the same time, smart meters for both electricity and water are making data available that are both highly resolved and granular.  New data and new ideas have potential to increase urban sustainability by integrating water and energy systems to a larger extent than is common today.

Scott’s current work uses empirical data to quantify the direct energy consumption embedded in residential water use, as well as assess key drivers and sensitivities for water + energy consumption.  This also includes disaggregation of whole-home water consumption data using methods based in probability, pattern recognition, and machine learning.  Other work has looked at pump scheduling heuristics and their applicability for demand charge management where electricity rates with demand charge time windows are present.


B.S., Mechanical Engineering, Magna Cum Laude, The University of Notre Dame, 2010

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