| description abstract | An adequately calibrated hydraulic model is critically to the water distribution digital twin, which requires accurate representation of a water distribution network (WDN) in near real-time. It is thus imperative to construct an extended-period simulation model that captures the network’s baseline normal and abnormal diurnal demand patterns, which are usually derived using monitored flow data. However, from a practical field aspect, it remains challenging to calibrate multiple demand groups of varying diurnal patterns in large-scale WDNs, as flow data are typically collected sparsely due to cost considerations, while pressure sensors are commonly deployed throughout the network. In this paper, we propose a new pressure-based demand aggregation and pattern calibration method that leverages on monitoring pressure data to aggregate demands, identifying abnormal consumptions, and calibrating the diurnal patterns of various demand groups. The new method is integrated with previously developed model calibration framework and applied to a large-scale WDN system having more than 330 km of underground water pipelines with weekly averaged pressure and flow data, as derived from a maximum historical period of nine months. Key findings from our case study analysis for the seven averaged days (Monday to Sunday) include: (1) calibrating the system’s flow balance to within 99% average accuracy by identifying and calibrating five unique demand patterns, inclusive of those associated with abnormal consumptions, via grouping 34 available pressure sensors; (2) calibrating the system’s energy balance to within 95% average accuracy by iterating the simulated pressures against representative monitored pressure profiles of the different demand groups during the flow calibration process; and (3) achieving an average 2.5% accuracy improvement for the overall energy calibration, relative to that of the previous calibration approach. Throughout the solution process, significant engineering judgment is adopted, coupled with optimization analyses, to calibrate the system’s flow and energy balances while meeting the model constraints and data availability. | |
