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Open AccessFeature PaperArticle

A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Department of Engineering, University of Ferrara, Via Saragat, Ferrara 44122, Italy
Centre for Water Systems, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK
Author to whom correspondence should be addressed.
Water 2017, 9(7), 507;
Received: 15 May 2017 / Revised: 6 July 2017 / Accepted: 7 July 2017 / Published: 12 July 2017
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. View Full-Text
Keywords: water demand; forecasting; Markov chain; stochastic water demand; forecasting; Markov chain; stochastic
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MDPI and ACS Style

Gagliardi, F.; Alvisi, S.; Kapelan, Z.; Franchini, M. A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain. Water 2017, 9, 507.

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