A probabilistic short-termwater demand forecasting model based on the Markov chain

Francesca Gagliardi, Stefano Alvisi, Zoran Kapelan, Marco Franchini

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)

Abstract

This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods), were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

Original languageEnglish
Article number507
JournalWater (Switzerland)
Volume9
Issue number7
DOIs
Publication statusPublished - 12 Jul 2017
Externally publishedYes

Keywords

  • Forecasting
  • Markov chain
  • Stochastic
  • Water demand

Fingerprint

Dive into the research topics of 'A probabilistic short-termwater demand forecasting model based on the Markov chain'. Together they form a unique fingerprint.

Cite this