**1. Introduction**

The study of demand side managemen<sup>t</sup> (DSM) is significant for the rational allocation of power resources and the improvement of terminal power e fficiency [1]. As an important means to solve the shortage of power supply and support energy development strategies, the long-term and e ffective implementation of power DSM can reduce environment pollution and enterprise costs, becoming an effective way to promote the sustainable development of society and the economy.

DSM focuses on improving the e fficiency of terminal power consumption and adjusting the mode of power consumption to reduce the dependence on power supply. Recently, intelligent DSM has attracted the attention of people [2]. The e ffective monitoring of load of users can provide data support for intelligent DSM, grasp the power consumption situation and users' behavior in real-time, and guide users to arrange the usage of power consumption reasonably, so as to improve the e fficiency of energy utilization [3].

Traditional load monitoring adopts an intrusive method via the installation of an information-collecting device to each user's electrical equipment. Although the collected data are accurate and reliable, the intrusive method has poor achievability due to the high-cost hardware, complicated installation in the early stage and low acceptance of users. In order to overcome this issue, non-intrusive load monitoring (NILM) [4] technology was proposed by Professor Hart in the 1980s. The non-intrusive way monitors the electrical signal only at the power supply point of the

user, and analyzes the categories of loads and corresponding operations of the user. The method not only reduces the economic cost of the device, but facilitates the installation for the user. However, monitoring in the non-intrusive way requires high accuracy of load identification.

Lin et al. [5] propose a transient signature extraction scheme under multi-resolution based on the S-transform, and use a 0–1 multi-dimensional knapsack algorithm for load identification. Hong et al. [6] extract seven kinds of load steady-state characteristics and their di fferent combinations to realize load identification. Liang et al. [7,8] use current waveforms, active and reactive power, other steady-state signatures and active transient waveforms for load identification. Adopting two-stage identification, He et al. [9] determine the type of electrical appliances by the steady-state characteristics in the first layer, and then distinguish the type of electrical appliances by the signatures of steady state and transient and user behavior mode in the second layer. This identification structure can divide complex problems into several simple problems and improve the e fficiency of the method.

Developing rapidly in recent years, intelligent learning provides a data-driven method to solve problems. Compared with the method of extracting signatures manually, the intelligent learning method can learn complex separable information from multiple dimensions automatically.

Tsai et al. [10] develop a new adaptive NILM system. It uses the k-nearest neighbor rule (k-NNR) and a back propagation artificial neural network (BP-ANN) recognizer to determine the type of each device based on transient signatures. Two low-complexity solutions based on combining k-means and a support vector machine are presented in [11]. Wu et al. [12] adopt the multi-label classification model, and the mixed current signals of multiple loads can be identified without energy decomposition. Chang et al. [13] extract the energy distribution signature of a single event's turn-on/-o ff transient signal based on Wavelet Multiresolution Analysis (WMRA) technology and Parseval's theorem, and then construct an artificial neural network through a back-propagation classification system. Finally, it classifies the load types according to the detailed energy distribution. Ruzzelli et al. [14] propose that the neural network is trained by signature library data and that online identification is realized by extracting load signatures in real-time. Lin et al. [15] present an IoT-oriented smart Home Energy Management Systems (HEMS) utilizing a novel hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO)-integrated NILM approach. Chang et al. [16–18] train the neural network by the characteristics of active power, reactive power and open transient energy. The method performs well, especially in the identification for loads with similar active and reactive power. Chao et al. [19] propose a semi-supervised NILM system that includes three steps. The transient characteristics of the load are extracted by a constructed one-dimensional convolutional neural network. The load identification can be e ffectively realized by linear programming boosting and an online process of parameter adjustment.

Existing NILM identification algorithms depend on the signature data, which need to be collected in advance. The monitoring process has a human interference, resulting in the interruption of the monitoring process in the actual operation process. Moreover, a pre-constructed library of load signature templates is fixed and single, meaning it has limitations to be used as a standard library for load signature comparison in di fferent users, a ffecting the accuracy of identification. Thus, there is an emerging need to achieve automatic monitoring of NILM algorithms and improve the universality of signature libraries to ensure accurate load identification.

Besides, the one-dimensional signatures of loads are more used in load identification algorithms. However, in the actual process of data acquisition, disturbances such as noise and harmonics on the grid side cannot be eliminated, which will a ffect the one-dimensional signatures of the load. Therefore, it is necessary to consider a more stable signature for identification, which can further improve the accuracy of identification.

In response to the mentioned problems, this paper proposes a new idea to solve the problem of NILM. It mainly includes two stages. Firstly, a dynamic load signature library is constructed adaptively for an independent user after the separation of mixed signals in the actual monitoring process. Then, the load identification is realized by the neural network based on the two-dimensional current data of the load in the library.

In addition, to further focus these problems in regard to NILM research, the contributions of the proposed method are as follows:

