In the paper, a Data Mining-based methodology to identify electricity consumption patterns that characterize the behaviour of end users in the low voltage active electric network of distribution network operators is proposed. It involves retrieving consumption data (active and reactive power profiles) from smart meters (at the central level, for example, house) and local sensors to collect information about household appliances and equipment to identify usage habits and preferences of end users. The methodology discovers the electricity consumption patterns and trends associated with each end user using data mining techniques based on clustering algorithms regardless of whether they are part of the end-users’ category. The proposed methodology used the data uploaded from the Pecan Street database sampled at 1 minute, ultimately identifying the consumption patterns of all equipment and appliances monitored at the level of end users. The patterns obtained will be integrated in the next stage where the energy consumption will be subject to an optimization process to reduce electricity bills for the end users.