## 1 Introduction  
The rapid growth of economic development has drastically increased electricity energy demands over the last few years. In order to meet this emerging growth, most electric utilities are upgrading their traditional power grids to more sophisticated, technically prudent and self-healing smart grid technologies [1]. It is now possible to monitor, manage and control the electricity energy demands on real time usage basis to enhance the efficiency of a power system network. For a substantial smart energy management framework, a reliable appliance load monitoring (ALM) system is important, which can ensure most risk-free and cost-effective energy consumption for the users. However, ALM can be executed by intrusive load monitoring (ILM) techniques that are relatively more accurate but require more sensing and measuring equipments and resources [1]. Another more immaculate way to execute ALM with less measuring device requirements is non-intrusive load monitoring (NILM). Non-intrusive appliance load monitoring (NIALM) or NILM determines individual energy consumption profile of different electrical appliances using a single measurement point. In the age of emerging smart grid technologies, sophisticated home energy management systems and efficacious utility infrastructures, NILM yields to be a crucial tool for reliable and inexpensive smart metering systems. The concept of NILM was first introduced by [PERSON] [2]. NILM methods for analyzing the energy signatures are based on three primary approaches--steady-state analysis, transient-state analysis and non-traditional appliance features [1; 2; 3; 4]. The steady-state analysis detects the changes in load identification considering stable states of devices, while the transient-state analysis focuses on the transitional states in energy consumption profile. The last approach concentrates on determining non-typical features of the electrical devices to disaggregate them. The detailed comparative study of these three NILM approaches is reported in [1; 3; 4]. NILM analyzes aggregate electrical load data together with appliance profile data to decompose the aggregate load into a family of appliance loads that can explain its characteristics [5]. From the inception of this concept, different methodologies have been and are being proposed to get significant improvement in reliable load monitoring and identification premises. To identify devices correctly, potential signatures extraction from the load data is necessary. Hence a novel non-intrusive load signatures extraction technique is proposed in [6], which first selects the candidate events that are likely to be associated with the appliance by using generic signatures and an event filtration step. It then applies a clustering algorithm to identify the authentic events of this appliance and in the third step, the operation cycles of appliances are estimated using an association algorithm. Another new concept of NILM techniques is presented in [7], where wavelet design and machine learning are applied. In this work, the wavelet coefficients of length-6 filter are determined employing procrustes analysis and are used to construct new wavelets to match the load signals. The newly designed wavelets are applied to a test system consisting of four loads and an improved prediction accuracy is observed comparing with the traditional wavelets based method. However, another wavelets based method reporting application of power spectrum of wavelet transform coefficients (WTCs) for load monitoring and identification is documented in [8]. Continuous wavelet transform (CWT) analysis to find feature vectors for switching voltage transients for NILM is discussed in [9], where support vector machines (SVMs) are trained by these features to identify loads. Orthogonal wavelet based NILM methodology is proposed in [10], where supervised and semi-supervised classifiers are used to automate the load disaggregation process. Most of the efforts attempt to disaggregate loads yield low sampling frequencies and steady consumption states.