# Comparative Study on Load Monitoring Approaches
## 2 Load Monitoring
### Non-Intrusive Load Monitoring (NILM)
Electrical devices introduce harmonics into the electrical current, which can be measured and analyzed to identify individual device loads [30]. Reactive power measurement: Reactive power is the portion of power consumption that does not contribute to work production. Electrical devices have different energy consumption behaviors for active and reactive power. Therefore, reactive power analysis can be used to identify individual device loads [28]. Event detection remains an optional step that is not always considered in the energy disaggregation process. Thus, two approaches to NILM have been developed, one event-based and one non-event-based [9; 31]. Once the stage has been considered in the energy disaggregation process, it contributes to the identification of events, such as on, off or any other change related to the operation of the devices. From the literature, [32] methods based on event detection can be grouped into three categories, i.e., based on expert heuristics; probabilistic models; and matched filters. The first one aims at splitting the normalized powers into stable and transient periods. Thus, it is established that stable periods are sets of fixed and contiguous samples, in which the input data do not vary more than the fixed threshold. The on/off transients are detected using the \"state change detection\" rule, which analyzes the series of total power differences and compares the values obtained to the predefined ranges for the start and end power changes (on/off) associated with each device. The second category, which refers to the probabilistic models, states that the identification of the precise time at which the event occurs is often referred to as change point detection. By introducing a generalized likelihood ratio, it calculates a \"decision statistic, starting from the natural logarithm of a probability distribution ratio before and after a possible change in the mean\". The third category refers to the use of matched filters, linking a known signal called \"mask\" with an unknown signal. The aim is to detect the presence of the mask in the unknown signal. In this way, the mask is identified with the power signals containing the transients of start-up or shutdown of the various devices, while the unknown signal represents the total energy consumption. However, let us add that [32; 33] point out that the addition of the event detection also contributes to:  
Increasing its accuracy: By identifying specific events related to the operation of appliances or devices, the energy disaggregation algorithm can more accurately attribute energy consumption to individual devices. Better visibility: Event detection provides more detailed information about how devices are used, which can be useful in identifying opportunities to reduce energy consumption or optimize energy use. Better understanding of device behavior: Event detection can provide information about how devices are used, which can be useful in identifying patterns and anomalies in device behavior. Nevertheless, errors can occur, such as false events or missed events, which could affect the quality of the expected result and thus contribute to degrading the performance of the overall NILM process. On the other hand, in the absence of event detection, the energy disaggregation process, as presented in Figure 5, ignores this step. As a result, the algorithms used, such as hidden Markov models and their variants [29; 34], directly process each sample of the overall voltage and current signal. In addition, removing the event detection step also has advantages, such as simplifying the energy disaggregation process, which can make it easier to implement and maintain; and reducing computational costs, as event detection can be computationally expensive, so removing this step can reduce the computational resources needed for energy disaggregation.