# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 1 Introduction  
Home energy management systems (HEMS) measure the energy consumption data of the household in real time for monitoring and optimizing the energy usage. Traditional intrusive monitoring systems collect fine-grained electricity data, posing higher initial cost for the homeowners since several smart meters must be plugged into the appliances. Non-intrusive load monitoring (NILM) systems allow to reduce hardware and maintenance costs since only one household-level smart meter is needed. As a consequence, the energy consumption at appliance-level is not available. NILM identifies a set of techniques that can disaggregate the power usage into the individual appliances that are functioning and disaggregate the consumption of electricity for each of them. NILM has been an active area of research in the last years. One of the earliest studies about the NILM system was developed by [PERSON] and [PERSON]. In [1] the authors classify different appliances applying clustering algorithm in the active-reactive power features plane. Since the publication of this algorithm in 1992, the field of load-disaggregation has seen a tremendous amount of further research and novel approaches, see [2, 3] for recent reviews. Furthermore, once the load has been disaggregated through a proper NILM technique, the disaggregated data can be used to improve the performance of some other important functionalities in the energy management system. This holds in particular for load demand forecasting [4], which may play a key role in the active management of modern distribution grid [3, 5]. As an example, the method proposed in [6] first disaggregates the overall power into single appliances, so that the consumptions of each appliance can be forecasted separately, and then forecasts the total power of an aggregated set of houses by aggregating the single predicted powers. These NILM-based methods allowimproving forecasting accuracy with respect to traditional methods that operate directly on the aggregate signal. The most promising approaches recently presented in the literature for load disaggregation are based on machine learning techniques. A machine learning system classifies the switching events associating them to a particular appliance, processing significant features from the measured electrical parameters. Solutions based on machine learning range from classic supervised machine-learning algorithms (e.g., support vector machines, artificial neural networks, random forests) to supervised statistical learning methods (e.g., K-nearest neighbours and Bayes classifiers) and unsupervised method (e.g., hidden-Markov Models and its variants). Recently, deep learning (DL) methods were also employed and seem promising for the most challenging problem posed by the consumption profiles of multi-state appliances [7; 8; 9; 10]. Commercial household smart meters usually provide the electric active and reactive power quantities measured at low frequency, typically on the order of a few values per second. As a consequence, these are the most widely used features and measurement rates reported in NILM-related literature. Since different appliances may show overlapping power values, performance is often unsatisfying. To overcome this issue, different features can be considered. Instantaneous samples acquired at high frequency (up to tens of kilohertz) have been proposed to extract information on the transient behavior of the supplied loads, but the drawbacks of these approaches are the increase in computational complexity and the real-time constraints associated with their implementation [3]. On the other hand, considering that the use of nonlinear appliances has increased continuously during the last decades, the frequency content of the involved electric signals can be exploited to improve the accuracy in the load identification process [11].