# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 1 Introduction
This implies the sampling rate used to acquired voltage and current signals must be sufficiently high to allow the frequency components of interest to be assessed correctly, but the measurement rate used in the disaggregation algorithm (i.e., the number of measured values per time unit, where each value is often obtained by processing a large number of instantaneous voltage and current samples), can be still in the order of few values per second, thus greatly improving the feasibility of these approaches with respect to the drawbacks mentioned above for the high-frequency solutions. Based on these considerations, to take into account the presence of non-sinusoidal power draws, several authors recently resorted to additional information, extracting extremely detailed features such as, for example, V/I trajectory, harmonics of different orders [12], the harmonic phase [13; 14], percentage total harmonic distortion (THD) [15], active and non-active current components [16], spectrograms of high-frequency current data [9]. The choice of the optimal set of features among them is crucial to maximize NILM effectiveness. The literature contains few contributions where the different features were applied simultaneously. In [17] six different signal signatures have been applied such as current, current harmonics, real and reactive power, geometrical properties of the V-I curve and instantaneous power, for ten appliances. [PERSON] et al. [18] combine single-feature single-algorithm disaggregation methods, processing current waveform, active/reactive power, harmonics, instantaneous admittance waveform, instantaneous power waveform, eigenvalues, and switching transient waveform. [PERSON] et al. [19] apply real and reactive power, harmonics, quantized currents and voltages waveforms and V-I binary image obtaining better results with respect to those obtained with features belonging to one category. In [20] the authors first explored a systematic approach of optimally combining features analyzing a plug load appliance identification dataset (PLAID) [21] retaining 58 features. Then, they performed several training sessions of the algorithm and the feature ranking and finally selected 20 features eliminating all of the voltage harmonics and all of the power-based features from transient state. The selected features included the total harmonic distortion (THD) of current, energy of detail wavelet coefficients at 2 nd scale,enclosed area by V-I trajectory, current root mean square, 3-rd, 5-th, 9-th and 11-th current harmonic coefficient, THD of nonactive current and normalized real power. In [22] the authors apply a complex hybrid energy disaggregation approach and propose a monitoring system consisting of a composite dual sliding window-based cumulative sum control chart (DSWC) to detect the transient event, a multi-layer Hungarian matching process to match load events. The detected events are converted to a bipartite graph optimization; a supervised clustering procedure is used to activate the corresponding category. Input data consist of rms current and voltage, active power, reactive power and the 15-th harmonic of the current. This paper focuses on the choice of a suitable set of features for a non-intrusive load monitoring system, providing the following main contributions:  
* different power terms, accounting for possible distortion in the voltage and current signals, have been considered as possible features;
* the power terms have been evaluated in different time intervals after the detected event;
* a systematic approach to rapidly obtain the lowest possible number of features preserving model performance has been explored applying two procedures of feature selection. The most effective feature sets are given as input to a multi-layer perceptron (MLP) classifier trained to identify the on/off events for each device. The procedure is applied to Building-Level \(\ell\)Ully-labeled for Electricity Disaggregation (BLUED) dataset [23].