# Comparative Study on Load Monitoring Approaches
## 2 Load Monitoring
### Non-Intrusive Load Monitoring (NILM)  
Non-intrusive load monitoring is the process of recognizing and disaggregating the total aggregated energy of electrical appliances into individual electrical consumptions from a single measurement point located at the electrical meter [20,23]. In other words, non-intrusive load monitoring is still the disaggregation of the total energy consumed by a building into individual electrical load consumptions. In this way, it is possible to easily determine the contribution of each switched-off or switched-on load to the overall consumption of a dwelling or any other type of building, by analyzing in detail the overall electrical voltages and currents of the loads in the system obtained from a single measurement point [24]. Unlike intrusive monitoring, which requires the installation of several devices, making installation complex and maintenance difficult [24], the non-intrusive approach is relatively simpler in its deployment and implementation [14]. Indeed, it requires only one measuring device to monitor the electricity consumption of the whole household, thus reducing its acquisition cost. Moreover, the installation is done very close to the main meter in the absence of any intrusion and requires almost no maintenance. Figure 3 gives an overview of the operating principle of this approach. Figure 3: Non intrusive load monitoring. Given the broader scope of NILM than ILM, users have more opportunities to monitor their devices both remotely and nearby [17]. Non-intrusive load monitoring (NILM) is an older research topic than intrusive load monitoring (ILM). The 1990s marked the beginning of the first scientific publications on the subject, notably with the work of [PERSON] [25]. Due to the technological revolution caused by artificial intelligence, ILM has seen a resurgence of interest, manifested by research work that has led to the development of numerous machine learning methods, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning, to name a few. For the scientific community, it is above all a question of finding solutions to the problem of energy disaggregation, which remains the main challenge. Figure 4 shows the number of publications on NILM over the last two decades. As recalled by [PERSON] et al. [26], in their work, the best way to disaggregate the total energy of loads is to design an algorithm that can be generalized to any type of building and is capable of operating in real time. In general, the algorithms used in non-intrusive load monitoring (NILM) are composed of the essential steps of data acquisition; event detection (optional step); feature extraction; and load identification, which again refers to load inference and classification, as can be observed in Figure 5.  
Data acquisition or electrical signal measurement is the step in which data about a building's overall energy consumption are collected using sensors, smart meters, or other monitoring devices. This data are then used for further disaggregation. NILM relies on  
Figure 4: Number of publications on non-intrusive load (NILM) from SciencesDirect database. Figure 5: Steps of Non-Intrusive Load Monitoring (NILM). non-intrusive data acquisition techniques, which means that it does not require sensors to be installed on the monitored devices. Methods and techniques have been developed to facilitate data acquisition. These include voltage and current signal measurement; harmonic analysis; and reactive power measurement. Voltage and Current Measurement: This method involves measuring voltage and current waveforms at the main electrical panel or other key points in the electrical circuit, using voltage transducers, current transformers (CTs), or Rogowski coils. The voltage and current waveforms are then processed to extract individual device loads [27; 28; 29]. Harmonic analysis: Harmonics are frequencies that are multiples of the basic electrical frequency (50 Hz in Europe).