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

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