**2. Related work**

In this section, some works related to the topic of efficient energy management in a Smart Home – Smart City environment are shown and discussed. The main differences between proposed measurement devices and their relationships, their sampling speed, type of operation, and resolution in the temporal measurement are highlighted.

The state of art shows different alternatives for obtaining the "consumer fingerprint" of electrical devices, an important element that motivates the development of this work, in such a way as to be able to propose an intrusive Smart Socket (SS) device that reaches improvements in the efficiency of device profile recognition and better use of home energy [1]. HEMS use monitoring techniques that are generally classified as Non-Intrusive Load Monitoring (NILM) and Intrusive Load Monitoring (ILM) [2].

#### *Characterization of the Electrical Consumption Pattern of Household Appliances for Home… DOI: http://dx.doi.org/10.5772/intechopen.110355*

The characteristics that differentiate these two techniques is that NILM only uses a monitoring point that is generally located at the entrance of the main power supply of the home electrical system (known as Smart Meter), while ILM uses sensor devices in each of the outlets (Smart Socket; Smart Plug SP) of all or main connected loads [3].

Generally, the data captured from the consumption of the devices is sent over the Internet and stored on a platform available in the cloud. These data mostly correspond to the active power demanded by the artifacts [4], which are processed in a Matlab® environment applying, for example, the Multi-Layer Back-Propagation Neuronal Network (MLBPNN) algorithm that allows detecting the real power profile. Consumed by each electrical device [5], so then the concept of digital footprint of device consumption [6] is proposed. On the other hand, NILM systems have the advantage of not interfering with the home circuit with expensive monitoring devices [7]. On the other hand, ILM systems, by having sensors in different loads of the circuit, provide more precise details about the consumption and alarms referring to each appliance [8], being able to make it possible to monitor very low-power devices and differentiate between variable consumption and consumption among a set of devices [9].

An SS sensor network is designed for use in work and home environments as a device, which is capable of identifying the appliance based on the behavior of the current patterns consumed by the electrical device [10]. The nodes communicate with each other using the environment's existing Wi-Fi, PLC, or ZigBee wireless architecture. Seamless sensor network integration is important for success in the realm of ubiquitous computing. The hardware and software architectures of the systems that analyze test devices are discussed and the consumption patterns and current profiles of home appliances are explained [11]. In this way, a possible hypothesis of this behavior is presented and the use of the classifier algorithm that does not need training, but only a dynamically updateable database, is explained, thus creating the need for a cloud database connected to all home endpoints. The system implementations and the description of the protocols developed for the device controls are also compared in the works studied [12]. Artifact pattern identification focuses on energy disaggregation and device recognition.

Refs. [13, 14] show the development of a smart system that analyzes the use of devices to extract user behavior patterns in a smart home environment. On the other hand, in [14, 15], it seeks to optimize the cost of electricity, where users can receive a warning of excessive consumption of devices connected to the home electrical system.

In [16, 17] the authors adopt a distributed metering system of smart outlets, designing an algorithm based on ANN (Artificial Neural Network) that exploits lowfrequency measurement data with power consumption every two minutes. The works [18–20] use an algorithm based on fuzzy logic that, by activating the calculation of parameters such as maximum power, average power, and cycle duration, obtains a result for the membership function, achieving the identification of the artifact by degrees of truth. However, the research works related to the automatic recognition of electrical devices described by [21, 22], carry out the sampling in the order of 15 to 10 minutes, since their objective is to determine the energy consumption according to the systems of billing.

We can also mention that the scientific publications on NILM approaches outnumber those of ILM [23], since the NILM approach proves to be much older than Mechanical Gauges that are almost a century of use [24, 25]. On the other hand, since the NILM (Smart Meter with prices of 300 to 400 USD) are based on the use of a single sensor, the installation of the detection system is simple and the data acquisition is of lower quality since we can only monitor general consumption in the home

[26, 27]. Device recognition using this type of approach generally suffers from precision due to the inherent problem of summation of consumption signals from different devices [28, 29]. On the other hand, with the ILM meter (SS, SP, price between 20 and 30 USD) the measurement is based on the use of multiple sensors inside the home, having a higher level of detail and resolution in the consumption data, which facilitates the identification of the electrical appliance [30].

In this work, the proposal of the Smart Socket ILM System, to monitor, detect and recognize the main household loads, using automatic measurement (Automatic Meter Reading – AMR) achieves a sampling rate of the order of 1/20 Hz, higher than the works [28–30] and with a resolution that allows not only the differentiation of each electrical appliance but also the activation of alarms, switching on and off in case of excess current (>16 Amps) or over-voltage (>230 Volts), enabling remote reconnection by the user once the alarm has been reviewed. Finally, the machine learning algorithm manages to discriminate each of the devices with great efficiency with a reduced calculation speed, sending information in real-time to the user and determining the electrical consumption of the home and of each of its devices.
