**Abstract**

For future smart cities, smart homes will be required. The key elements are the smart use of energy and smart communication systems that are connected to homes. Along with this, the devices inside the house will need to be monitored and managed efficiently. One of the current proposals is the use of Home Energy Management Systems (HEMS) allowing to solve problems associated with efficient management, the economy of electrical energy, and failures/alarms regarding the operation and safety of appliances. This work proposes a model for the recognition of patterns of energy consumption in household appliances, based on the capture of electrical parameters through Smart Socket, using an intrusive method in the electric charge. The data acquisition system corresponds to an IoT platform that uses automatic meter reading elements, which, connected via Wi-Fi, send data to a cloud service. The results obtained allow a characterization of household appliance consumption profiles, with high levels of reliability and under multiple operating states. Because of the foregoing, the detection, monitoring, and control of household appliances connected to the electrical network allow the reduction of both household billing and CO2 emissions.

**Keywords:** automatic meter Reading (AMR), home energy management systems (HEMS), intrusive load monitoring (ILM), pattern recognition, smart socket (SS)
