Developing a stochastic simulation model for the generation of residential water end-use demand time series

: Smart metering technologies allow for gathering high resolution water demand data in the residential sector, opening up new opportunities for the development of models describing water consumers’ behaviors. Yet, gathering such accurate water demand data at the end-use level is limited by metering intrusiveness, costs, and privacy issues. In this paper, we contribute a stochastic simulation model for synthetically generating high-resolution time series of water use at the end-use level. Each water end-use fixture in our model is characterized by its signature (i.e., its typical single-use pattern), as well as frequency distributions of its number of uses per day, single use duration, time of use during the day, and contribution to the total household water demand. The model relies on statistical data from a real-world metering campaign across 9 cities in the US. Showcasing our model outputs, we demonstrate the potential usability of this model for characterizing the water end-use demands of different communities, as well as for analyzing the major components of peak demand and performing scenario analysis.