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.
Highlights
Water distribution systems fulfil the fundamental task of satisfying the water demand of users, and their long-term and short-term management can be supported by the use of water demand forecasting models
A new approach to short-term water demand forecasting based on the Markov chain is presented in this paper
The application of a homogeneous and non-homogeneous Markov chain gave rise to two models, which make it possible to estimate the probabilities of future water demand falling within pre-established classes/intervals and, based on these probabilities, provide a deterministic forecast of the future value
Summary
Water distribution systems fulfil the fundamental task of satisfying the water demand of users, and their long-term and short-term management can be supported by the use of water demand forecasting models. Forecasts of daily or hourly demands over limited time horizons (for example a week or the 24 h) can provide useful information for planning short-term or real-time management of the devices at the service of water distribution systems, such as the planning of pumping station operations, the control of valves, etc. Depending on their features, water demand forecasting models can be divided into different categories.
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