# Use of Binary Classification in Non-Invasive Load Monitoring
## 2 NILM--Key Elements
### Research Methodology  
NILM research is divided according to the many different categories mentioned earlier. It seems worthwhile to further subdivide research into the following:  
* Global, covering multiple classes of equipment represented by many different units operating in different states and under different power conditions;
* Local, covering individual devices or treating devices of the same type as other devices. Many authors use a local approach in their research, treating each piece of equipment as a different device. This approach is easier to implement and yields better results but is practically impossible to generalize because of the wide variety of individual electricity consumers. Most often, in NILM-related works, the selection of identifying features is expert in nature [5; 6; 13; 14] and mainly includes the active power (P), reactive power (Q), and root mean square (rms) current (I). The harmonics of the active power, reactive power, and current are used less frequently, as their use directly leads to the 'curse of dimensionality', the avoidance of which requires the use of analytic feature selection methods. In [24], [PERSON] and coauthors proposed a multilabel classification method using the random forest method for load identification. Multilabel classification makes it possible to determine the category to which the data belong and allows for the identification of devices in different operating states on the basis of aggregated signals. For the study, the authors used five electrical appliances (two computers of different brands, a printer, a kettle, and a microwave oven) on the basis of data from the laboratory and the BLUED (Building-Level fUly-labeled dataset for Electricity Disaggregation) database. The way the devices were classified and their number indicate that the proposed research should be localized. To build the model, the authors used basic electrical parameters, such as the rms current, current signal peak factor, maximum current, active power, reactive power, power factor, fundamental harmonic current, and third harmonic current. In the next step, they examined the relevance of each characteristic to the model being built. The rms value of the current and its third harmonic component have the greatest impact on the results, reaching 20%, whereas the reactive power has a small impact of only 1%. The contributions of the rms value of the current and the fundamental harmonic are 16% and 18%, respectively. The contributions of the other parameters are less significant and amount to an active power of 10%, power factor of 9%, and a peak factor of 6%, obtained by the authors, as well as a very high identification accuracy, with an accuracy (6) close to 98%, using expertly selected parameters. [PERSON] and colleagues [13] presented a method using a convolutional neural network (CNN) for electrical load identification, whose operation is based on the use of deep learning (DP). The CNN used in this study consisted of seven layers. Five appliances were identified--microwave oven, oven, induction hob, toaster, and light--by defining 10 classes (on/off for each) and a no event class. The proposed algorithm corresponded to whether the appliance was on or off. The authors performed tests on data acquired from their own measurement system operating at 10 kHz. Their proposed algorithm showed an error of 1.84% when identifying switch-on events and an error of 1.21% when identifying switch-off events. This means that its accuracy index (6), which describes classification accuracy, was 98%. To compare the results obtained, an experiment was conducted on the BLUED public database. The authors expanded the number of classes to 69 (34 device types), obtaining an accuracy of 87.9%. The number of classes extracted for the BLUED database (not currently available) covering measurements made in a single building indicates that the authors used a local approach. In [16], the authors used the k-nearest neighbors method to identify appliances for the NILM.