The large volume of electricity consumption data calls for the aggregation of this data. The implementation of aggregation methods is therefore a major concern to which an answer is given by presenting a case of aggregation of electricity consumption data using the jump process. A data set made it possible to carry out simulations and to present the results obtained for the daily, monthly and annual aggregations. The principle of using the jump process for the approval of these data is highlighted. This work is a concrete presentation of a simulation for the aggregation of electricity consumption data in a network of wireless sensors that can constitute a network of smart meters. The approach of this work consists in using aggregation methods to reduce the flow of data exchanges in wireless sensor networks. In fact, this work highlights several interesting properties that justify the choice of the jump process including flexibility, modeling of rare events, management of uncertainties adaptability to non-stationary data management of fluctuations in demand, consideration of volatility effects and scalability. Many significant impacts are expected, including improving network stability, optimizing resource management, reducing operational costs, integrating renewable energies, and data-driven decision-making. The jump process also presents limitations including modeling complexity, model calibration, computational complexity, interpretability of results, uncertainty management.