# Comparative Study on Load Monitoring Approaches
## 2 Load Monitoring
### Non-Intrusive Load Monitoring (NILM)
Feature extraction involves extracting relevant features from the collected data, such as energy consumed, current, voltage, frequency, and other parameters that can be used to distinguish between different devices, such as harmonics. These features are used to feed the machine learning algorithms in the next step. In addition to the previously mentioned features, the statistical characteristics of the electrical energy can also be used to train machine learning algorithms, such as k-NN, decision trees, and neural networks, to predict the energy consumption of each device. [PERSON] et al. [35] reported that the use of statistical features can help achieve a high performance in detecting electrical appliances, with accuracy rates up to 99% for some appliances. Load disaggregation refers to the training of a machine learning algorithm using the extracted features and the data collected in the previous steps. There are three methods of machine learning: supervised learning, unsupervised learning, and deep learning [36; 37]. In the supervised learning approach, the loads undergoing disaggregation of consumed energy are labeled. The electrical signals collected during the data acquisition phase are then used to train the device signatures. Regarding the supervised learning algorithms usually used in the NILM process, the literature provides more information about them. For example, k-Nearest Neighbor (kNN) [38; 39], support vector machines (SVMs) [40; 41], decision trees [42], genetic algorithms [43; 44], artificial neural networks (ANNs) [45; 46], hidden Markov models (HMMs) [29; 34], Naive Bayes classifier [47; 48], and deep learning neural networks that are used in NILM as supervised learning algorithms. In addition to the fact that these algorithms have demonstrated their ability to identify devices and allocate the total energy consumed, they nevertheless display limitations when implemented in real time. Because supervised learning algorithms cannot adapt to changes in the environment, updates to device signatures must be made based on a number of factors, such as aging of devices, decrease in device performance, replacement or addition of a device in the home or building, and power line disturbances. Unsupervised learning algorithms do not require labeled data to disaggregate the total energy consumed. Of the most common algorithms in the literature dealing with NILM problems, we can cite hidden Markov models (HMMs), k-means clustering, and expectation maximization (EM) [37; 49]. For HMMs, the identification of the device state (on/off) and energy consumption (active and reactive power) are respectively an assimilated concept of hidden events and observed events of hidden Markov models. As for the transitions of the device state, they are observed through the transition matrix. Although unsupervised learning algorithms compete with supervised learning algorithms, both still require strong features that can contribute to the performance of NILM systems. These features can be obtained by extracting device-independent characteristics through feature matching and transformation techniques. In recent times, deep learning algorithms have shown the ability to extract device-specific features from raw data without using any expertise. Given the interest in deep learning algorithms, precisely in terms of their ability to solve complex problems in applications, such as speech recognition, computer vision, and asset condition monitoring, many researchers have, in the context of NILM, undertaken to improve the identification of devices, in order to make energy disaggregation algorithms more efficient. This is the case for algorithms, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and autoencoder (AE) to automatically extract the eigenfeatures from the aggregated signal, in order to improve device classification and energy disaggregation [50; 51]. Deep neural networks (DNNs) have been used as a multi-classifier to identify individual devices [52].