The smart meter can process sensor data in a residential grid. These sensors transmit different parameters or measurement data (index, power, temperature, fluctuation of voltage and electricity, etc.) to the smart meter. All of these measurement data can come in different ways at the smart meter. The sensors transmit each measurement data to the smart meter. In addition, the collection of this data to a central system is a significant concern to ensure data integrity and protect the privacy of residents. The complexity of these data management also lies in their volume, frequency, and scheduling. This work presents a scheduling and a collection mechanism in private power consumption data between both sensors and smart meters on one hand and between smart meters and the central data collection system on other hand. We have found several approaches to intelligent meter data management in scientific researches. We propose another approach in response to this concern for the scheduling and collection of measurement data to a central system from residential areas of sensors’ network connected to smart meters. This work is also an example of a link between data collection and data scheduling in intelligent information management, transmission, and protection. We also propose a modeling of the measurement objects of smart grid and highlight the changes made to these objects throughout the process of data processing. It should be noted that this smart grid system consists of three main active systems namely sensors, smart meters and central system. In addition to these three systems, there are other systems that communicate with the smart meters and the central system. We have identified three implementation models for the smart metering system. We also present an intelligent architecture based on multi-agent systems for the smart grid. Most current electricity management systems are not adapted to the new challenges imposed by social and economic development in Africa. The objectives of this study are to initiate the design of a smart grid system for the management of electricity data.

This work aims to seek a pragmatic approach to assess electricity consumption at the level of households, buildings and neighborhoods. The main concern consists in proposing aggregation methods based on jump process according to a customer environment that is intrinsically linked to the implementation of a centralized system. The aim of the approach is to present data aggregations that derive their basis from a data model in order to facilitate the processing of electricity data at different scales of analysis. Such a smart meter data management process merits the design of an aggregated database that can store data for a house, a building and a neighborhood. The advantage of this system lies in the facilitation of data interpretation and the ability to guide decision-makers in the management of electricity consumption. An analysis of the behavior of electricity consumption is also proposed based on the monitoring of the electricity consumption of the various devices connected to a smart meter.