## 1 Introduction  
With energy consumption growing year after year, carbon emissions have become an important issue for many countries. In order to considerably reduce their annual carbon emissions by 2050, energy conservation has been a national important aim. For this reason, current electricity infrastructure is transitioning to smart grid, in which information flowing between energy generation and consumption points is obtained from dedicated devices called smart meters. As popular electricity measuring devices, smart meters will be collecting vast amounts of electricity consumption data. However, the data from smart meters are mainly household total electricity use, and the energy consumption of a single appliance will remain unavailable. For market utilities, detailed electricity information can improve the power grid planning scheme and ensure the safe and economic operation of power grids by load forecasting. In addition, elaborate feedback of appliances' consumption creates more savingsin electricity [1,2] for consumers. Non-intrusive load monitoring (NILM), which disaggregates a household's aggregate electricity consumption to individual appliance [3], has been greatly developed. The NILM approach research focuses on two issues. The first is the feature sets adopted to identify appliances. A variety of features have been extracted and applied, such as the apparent power [4, 5, 6, 7, 8], current and voltage [9], active and reactive power [10, 11], the power phase angle and power factor. Current features include transient and steady-state features. Steady-state features are observed when a state transition of appliances has been completely over, and transient features are extracted when a state transition is in progress. As higher sampling rate data of kHz are required, transient features need more complex and costly hardware equipment [12]. Except for the time domain features above, Fourier analysis and combination with other features of the time domain are used to identify appliances [13, 14, 15]. Discrete wavelet transforms of power were employed in References [16, 17] and Reference [11]. The second issue for NILM is learning algorithms applied to NILM. In literature, k-nearest neighbor (k-NN) [18, 19] and neural networks [14, 20] are widely applied to aggregate total consumption. Decision trees were used in References [21] and [16]. Support vector machines have mainly been used as classifiers [22, 23, 24]. The naive Bayes classifier is occupied to identify the different combination value of various appliances [25]. In recent years, deep learning based approaches have worked to load disaggregation [26, 27]. In addition, a similarity measure between two sequences plays an important role in classification [28]. Dynamic time warping (DTW) has been employed to measure similarity, which can overcome time distortion [29]. To that end, although the existing NILM methods have achieved a good effect, there are still many problems discussed in recent works. The first, NILM technology using transient or high-frequency steady-state features, requires additional hardware equipment installed [30], which leads to high hardware cost. Smart meters are able to read 10-second resolution data. Thus, using electricity consumption data in 10-second resolution or even more is a benefit for the industrialization of NILM technology. Secondly, event-based methods in literature are not suitable for features from low sampling rate data [5, 8, 9], because overlapping events inevitably happen, and the events of low power consumption appliances are difficult to detect due to the power variety of high-power consumption appliances. Factorial hidden Markov models (FHMM) have been used to solve the problem [31]. However, FHMM needs prior distribution and has high computational complexity of the algorithm [32] for multi-state appliance. The third problem to NILM methods is the poor power estimation accuracy caused by the power variety of appliances when operating. Generally, average powers are considered as the estimation value of power [9].