## 1 Introduction
To achieve a better accuracy, it is necessary to consider higher sampling frequencies and both steady states and transient states. In regard to this phenomenon, a novel event-based detector for NILM applications is reported in [11]. This method takes into account small power changes which are not usually well detected on most low-frequency systems and it is based on the application of Hilbert transform to obtain the envelope of the signal. Microscopic characteristics collected from current and voltage measurements are employed to detect electrical devices in [12]. Another novel approach based on linear-chain conditional random fields (CRFs) combining current and real power signatures for efficient NILM solution is reported in [13]. Phase noise of individual device as a new load signature characteristic is discussed in [14]. However, a novel particle-based distribution truncation method with a duration dependent hidden semi-Markov model for NILM is presented in [15]. In [16], gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network in where the partially overlapped output sequences are averaged to produce the final output of the model. However, a number of intricate signal processing techniques based NILM solutions are proposed in [17; 18; 19; 20; 21]. A few works on NILM applying computationally complicated deep learning techniques are documented in [22; 23; 24; 25; 26; 27; 28; 29]. A demand side personalised recommendation system (PRS) is proposed in [30] that employs service recommendation techniques to infer residential users' potential interests and needs on energy saving appliances. The proposed scheme starts with the application of an NILM method based on generalised particle filtering to disaggregate the end users' household appliance utilisation profiles achieved from the smart meter data. Then, based on the NILM results, several inference rules are applied to infer the preferences and energy consumption patterns. In [31], an improved NILM technique is proposed, which comprises a shunt passive filter installed at the source side of any residential complex. The first step is to determine the harmonic impedance at the load side for different groups of loads for a single household, whereas the second step is to implement a fuzzy rule-based approach for identification of different loads at the consumer end. In [32], a statistical features based NILM solution is proposed, which applies a number of supervised classifiers on current measurements and evaluates performance comparison for device disaggregation. From the literature review, it is yielded that different novel and sophisticated analysis methodologies are proposed till date for efficient NILM solutions. This article proposes another novel approach for features extraction from the load signatures, referred to as current shapelets. Shapelets are generally defined as interpretable time-series subsequences of a dataset, which can categorize an unlabeled sequence if it comprises an occurrence of the shapelets within a specified learning distance. The fundamental concepts of shapelets are elucidated in [33; 34; 35]. Shapelets are utilized by many researchers in computer science and engineering to develop robust, compact and fast classification models. However, in this work, the analysis starts with a keen observation of the normalized current data of the devices present in the controlled on/off loads library (COOLL) NILM public domain dataset [36]. A 5-point moving average filter is applied to remove high frequency spikes. Electrical devices can be turned on or off at any time instant. When a device is turned on, there happens to be a prominent change in the current value and a peak point is reached. This is true for each cycle of operation. After a few instants, the current becomes stabilized, which can be inferred as the stable phase of a device current.