A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain
AbstractThis 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
Gagliardi F, Alvisi S, Kapelan Z, Franchini M. A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain. Water. 2017; 9(7):507.Chicago/Turabian Style
Gagliardi, Francesca; Alvisi, Stefano; Kapelan, Zoran; Franchini, Marco. 2017. "A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain." Water 9, no. 7: 507.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.