# Comparative Study on Load Monitoring Approaches
## 2 Load Monitoring
### Intrusive Load Monitoring (ILM)  
Intrusive load monitoring (ILM) is a technique used to identify, locate, and monitor the power consumption of each device in a given system using wireless sensors or smart plugs [20; 21]. Based on this definition, it can be deduced that the number of devices to be monitored is equivalent to the number of sensors to be installed. In energy management systems, ILM is often used to help identify energy usage patterns and thus improve energy efficiency. In addition, it can also be used in residential, commercial or industrial environments to track device power consumption, providing granular data that can help identify energy waste and optimize energy use. For ILM, the main objective remains the automatic identification of electrical appliances by means of pre-identified tags. In this way, ILM can provide accurate and satisfactory results on the consumption status of each device. As seen in Figure 1, the different stages of intrusive load monitoring are shown. Although the accuracy of the feedback of the consumed electrical energy remains the strong point of this approach, the implementation can be quite costly due to the high acquisition cost of the smart plugs or sensors required for the system [21]. In addition, ILM requires a sub-metering system, as the consumption measurements made on each device all converge to a main meter that centralizes the consumption information of each load. As a result, there is an interdependence in terms of functionality between the different sub-meters, which, in case of failure of one of these sub-meters, could hamper the communication of results, affecting the accuracy. Therefore, careful planning and management of the sub-metering system are essential to ensure that intrusive load monitoring works properly. Figure 2 illustrates the operating principle of intrusive load monitoring. The intrusion level of monitoring meters can be determined by considering the number of loads to be connected to the measuring devices, [PERSON] et al. [22] in their work organized intrusive load monitoring into three (03) subdomains which are distinguished by their levels of intrusion. Indeed, the first subdomain called \"ILM1\" refers to submeters installed between the main meter of the electricity supplier and the main circuit breaker. This type of installation is generally used to monitor a part of the house. The second sub-domain, named \"ILM2\" refers to the metering devices installed at the outlets. In this case, the devices directly connected to the socket or power strip are monitored. The third sub-area, \"ILM3\", refers to the case where the monitoring device is integrated into the device or placed at the socket. These applications are accessible because they are based on technologies that use data, signal processing, statistics, and machine learning. The collected data for the case of ILM labelled and a pattern matching through the signals of known and unknown loads is performed. Given the complexity of implementing the intrusive load monitoring, the non-intrusive load monitoring approach is positioned as an alternative solution due to its ease of implementation. However, it remains an older technique than ILM. It was developed in the early 1980s by [PERSON], who, for reasons of energy saving, ventured into work that led to the birth of this concept. Intrusive load monitoring relies, as much as the second approach, on data sets constituted either by labelling (supervised learning) or by not labelling (unsupervised learning). According to [PERSON] et al. [5], existing ILM techniques depend on labels entered by end users and are usually subject to a supervised learning scheme. It turns out that in the real  
Figure 1: Steps of Intrusive Load Monitoring (ILM). Figure 2: Intrusive Load Monitoring. world, device labelling is laboriously done by the consumer, and thus training data are insufficient for supervised learning models. Load identification is an essential aspect, as much as consumption control. [PERSON] et al.