Event Date
Jorge E. Pesantez is an Assistant Professor at Fresno State University. He obtained his M.S. and Ph.D. in civil engineering from North Carolina State University. Before joining Fresno State, Jorge worked as a Postdoctoral Research Associate at the University of Illinois Urbana-Champaign. His research focuses on urban water infrastructure systems, combining novel data sets from advanced metering infrastructure with AI methods.
Abstract
Residential water demand exhibits pronounced diurnal patterns, with peak-hour consumption imposing significant operational and management challenges on urban water systems. These demand peaks increase system costs due to the elevated energy requirements for water treatment and distribution. Moreover, accommodating high peak demands necessitates substantial infrastructure investments to support urban growth and economic development. This study develops an agent-based modeling (ABM) framework to evaluate a demand-side management strategy aimed at shifting peak-hour water use. We focused on calibrating the ABM that simulates the effectiveness of a user-feedback-based water conservation program using real-world data from a partner water utility. Model calibration was achieved by adjusting household-level parameters, including peak-hour demand, responsiveness to the feedback system, and demand reduction factors, to align simulated outputs with observed smart meter data. The calibrated ABM achieved percent errors ranging from 0.8% to 3.7% across scenarios. The resulting model provides water utilities with a decision-support tool to assess the potential effectiveness of user feedback–driven conservation programs and to tailor such strategies to local conditions.