Assessment of the predictive uncertainty within the framework of water demand forecasting by using the Model Conditional Processor

An application of the Model Conditional Processor (MCP) is here presented to assess the predictive uncertainty in water demand forecasting related to water distribution systems. The MCP allows for the estimate of the forecast and its uncertainty through the elaboration of the forecasts provided by more than one deterministic forecasting model. The approach is applied to the hourly water consumptions of a town in the northern Italy and the results highlight its effectiveness, provided that the data set used for the parameterization is effectively representative of the accuracy of the real time water demand-forecasting model.