Abstract
Abstract 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
