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In order to improve low voltage (LV) network visibility without extensive monitoring and integrate low carbon technologies (LCTs) in a cost-effective manner, this paper proposes a novel three-stage network load profiling method. It uses real-time information monitored from selective representative areas to develop network templates. The three stages are: clustering, classification and scaling. It can be used to identify the loading conditions of unmonitored LV systems with similar fixed data to those monitored LV substations. In the clustering stage, hierarchical clustering and K-means are used to cluster substations into groups based on the shape of the monitored load profiles. The classification tool designed with multinomial logistic regression maps an unmonitored LV substation into the most probable templates by using routinely available fixed data. Finally, clusterwise weighted constrained regression is employed …