AbstractThe estimation of nodal water demands for water distribution systems has been extensively researched over the last decades. However, demand estimation performance is dependent on selected nodal demand aggregation and available sensor locations. Despite a variety of water demand clustering approaches already proposed, a comprehensive methodology capable of generating cluster solutions with practical interpretation and maximum accuracy is still lacking. To achieve that goal, the current research presents an innovative clustering methodology based on network flow measurements. The procedure follows two primary steps: (1) determination of optimal flow sensor locations, and (2) an integrated approach for cluster identification, which includes cluster scenario generation, demand estimation, and identification metrics. The effectiveness of the proposed method was tested on a synthetic case study with realistic generated spatial patterns. Results demonstrate that finding a high-quality cluster solution is possible by utilizing (1) additional flow sensors installed according to the proposed V-optimal early split methodology, and (2) cluster selection based upon a likelihood metric. In general, the metrics used for both sensor location and cluster identification were found to be critical to identifying the best set of clusters.