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.

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

GAGLIARDI, Francesca
Primo
;
ALVISI, Stefano
Secondo
;
FRANCHINI, Marco
Ultimo
2017

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.
2017
Gagliardi, Francesca; Alvisi, Stefano; Kapelan, Z.; Franchini, Marco
File in questo prodotto:
File Dimensione Formato  
2017_Water_Gagliardi.pdf

accesso aperto

Descrizione: Articolo
Tipologia: Full text (versione editoriale)
Licenza: Creative commons
Dimensione 2.11 MB
Formato Adobe PDF
2.11 MB Adobe PDF Visualizza/Apri

I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2374602
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 45
  • ???jsp.display-item.citation.isi??? 38
social impact