# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
###### Abstract  
Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks. n _article_supplementary  
categories: steady-state and transient state. It is a good idea to complete NILM with unsupervised learning by combining different features [4]. The most commonly used steady-state signatures are active and reactive power [2; 5]. They are effective in identifying high-power devices, but it is challenging to separate low-power appliances for them due to the possibility of power overlap. Later works extended the steady-state signature to many aspects, such as harmonics [6], current and voltage waveforms [7], voltage-current trajectory [8; 9; 10], inactive current [11] etc. All of them can disaggregate certain types of appliances effectively. In order to define more accurate load signatures, features are extracted from the period of two stable operations, called transient signature [12; 13; 14]. Due to a relatively shorter duration of transient signatures, the probability of feature overlapping is lower. Transient signatures require a high sampling rate. However, it is difficult to achieve high sampling rate in practical applications, which limits the practicability of transient signatures. With the large-scale deployment of smart meters, NILM approaches that work with a lower sampling rate have drawn increasing attention. Most smart meters installed in practical applications measure and transmit the power signals at a relatively low frequency, generally between 1 Hz and 1/900 Hz [15]. Consequently, the steady-state signatures become a more suitable choice in applications. Low-rate NILM methods can be divided into two categories. One refers to event-based NILM [16], which implements load monitoring by classifying the signatures related to load events. The other is state-based NILM [17], which realizes load disaggregation through pattern recognition. Most of state-based NILM methods are based on the hidden Markov model (HMM) and its variations [18; 19; 20; 21] due to the strong ability in modeling the combination of stationary process with continuous valued data over discrete time. [PERSON] proposes a load disaggregation method based on clustering algorithm and support vector regression optimization, which works very well [22]. Four different extensions of HMM are presented [20], but they are likely to converge to a local minimum. To address this problem, the hierarchical Dirichlet process hidden semi-Markov mode (HDP-HSMM) is described [21]. To extend NILM service to new households without further intrusive monitoring, a model fitting algorithm is designed [23], which adopts iterative k-means to fit a HMM with only one typical duty cycle of device. However, HMM has heavy dependence on clean transitions from one state to another, especially for continuously varying appliances. To alleviate this problem, a sparse coding method based on structured prediction is developed [24]. Motivated by the success of deep learning, a deep sparse coding is proposed [25].