Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)

9 pages•Published: September 20, 2018

Peyman Yousefi, Gholamreza Naser and Hadi Mohammadi

Abstract

A comprehensive understanding of water demand and its availability is essential for decision-makers to manage their resources and understand related risks effectively. Historical data play a crucial role in developing an integrated plan for management of water distribution system. The key is to provide high-resolution temporal-scale of demand data in urban areas. In the literature, many studies on water demand forecasting are available; most of them were focused on monthly-scales. Since monitoring of time series is a prolonged and costly procedure, the popularity of disaggregation methods is a most recent desirable trend. The objective of this research is to transfer low-resolution into high-resolution temporal scale using random cascade disaggregation and non-linear deterministic methods. This study defines a new technique to apply previously proposed random cascade method to disaggregate continuous data of the city of Peachland. The accuracy of the results is more than 90%. It represents a satisfactory application of the models. The proposed approach helps operators to have access to daily demand without acquiring high-resolution temporal scale values. Although the disaggregated values may not be precisely equal with observed values, it offers a practical solution for the low equipped WDS and leads to lesser number of drinking water-related problems.

Keyphrases: disaggregation, embedding dimension, peachland, random cascade, temporal scale, water demand

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 2408-2416.

Links:	https://easychair.org/publications/paper/vL16
	https://doi.org/10.29007/4vfl

BibTeX entry

@inproceedings{HIC2018:Estimating_High_Resolution_Temporal,
  author    = {Peyman Yousefi and Gholamreza Naser and Hadi Mohammadi},
  title     = {Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/vL16},
  doi       = {10.29007/4vfl},
  pages     = {2408-2416},
  year      = {2018}}

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