Comparison of parametric and nonparametric disaggregation models for the top-down generation of water demand time series. Issue 1 (2nd January 2016) - Alvisi, S.
ABSRACT: This study presents a 'top-down' procedure for generating synthetic time series of hourly nodal water based on the application of disaggregation models already presented in the literature in the field of hydrology. More specifically, a parametric and a nonparametric disaggregation model are compared to assess their performance in reproducing, on a nodal level, the main statistics of the time series of historically observed water demands. Moreover, with regard to the nonparametric model, a variant of the original formulation is proposed with the aim of improving the ability to reproduce the lag–1 temporal correlations of the water demand time series generated by disaggregation. The proposed procedures were evaluated with reference to a case study based on a time series of the water demands of 21 users of the water distribution system of the town of Milford (Ohio). The results obtained showed that both the parametric and nonparametric models enable water demand time series to be generated which are statistically similar to the time series observed, thus representing a valid tool for generating synthetic series of nodal water demands from a spatially aggregated time series using a top-down approach.