The methodology proposed here is aimed at providing both the deterministic water demand forecast and an estimation of the uncertainty affecting this forecast. Two models based on non-homogeneous and homogeneous Markov Chains (named Markov Chain A - MCA and Markov Chain B – MCB, respectively) are developed. These models are applied to three real case-studies with the aim of forecasting hourly water consumptions up to 6 hours ahead. The results obtained show that model based on the homogeneous Markov Chain (MCB) provides more accurate short term forecasts than the non-homogeneous Markov Chain model (MCA) and concurrently is capable of providing useful information about the forecasting uncertainty.
Markov Chain based model for probabilistic short term water demand forecasting
GAGLIARDI, Francesca;ALVISI, Stefano;FRANCHINI, Marco
2016
Abstract
The methodology proposed here is aimed at providing both the deterministic water demand forecast and an estimation of the uncertainty affecting this forecast. Two models based on non-homogeneous and homogeneous Markov Chains (named Markov Chain A - MCA and Markov Chain B – MCB, respectively) are developed. These models are applied to three real case-studies with the aim of forecasting hourly water consumptions up to 6 hours ahead. The results obtained show that model based on the homogeneous Markov Chain (MCB) provides more accurate short term forecasts than the non-homogeneous Markov Chain model (MCA) and concurrently is capable of providing useful information about the forecasting uncertainty.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
