# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
###### Abstract
However, a typical shortcoming is that more parameters need to be learned for going deeper. [PERSON] uses a neural network to complete the load disaggregation problem and gets good results [26]. But generally, state-based algorithms have a common drawback, i.e., long periods of training and high computational complexity, which makes them difficult to apply to real-time disaggregation. Event-based algorithms have a relatively fixed processing procedure, including event detection, feature extraction and event classification. To obtain accurate identification results, different classification techniques are tried, including k-means [27], k-nearest neighbor (k-NN) [28], naive Bayes [29], maximum likelihood [30] and decision tree (DT) [31]. In [30] the maximum likelihood classifier is designed to disaggregate load based on the power profiles, but it only works for single-state loads. [PERSON] relies on graph signal processing (GSP) to realize the edge detection, clustering, and pattern matching [31]. However, experimental results show that power fluctuation or a close power range of appliances will influence algorithm performance. Qi adopts graph shift quadratic form constraint to complete low-rate load disaggregation [32]. A novel combined k-means-SVM-based NILM method is developed [33]. However, event-based methods face a common challenge, that is, most of the existing algorithms only rely on a two-dimensional feature space of active and reactive power for load identification without considering other additional features, such as time and sequence signatures. Moreover, the same type of appliances in different households have quite different signatures, so it is unsuitable to use a unified model to represent them. The existing NILM methods are focused on detection of all appliances without considering the applicability of load disaggregation in realistic applications, that is, there is no definition of an accurate load space related to the actual application. Load space refers to the range of load types to be analyzed. In this paper, we define different load types that need to be analyzed. It also describes which loads belong to different types. Because of the complexity of the original load space, it is impractical to identify all devices based on a one-dimensional aggregated signal. So, there is an emerging need to define a suitable load space. In order to address the difficulty of identifying appliances with similar power, a linear discriminant classifier group considering multidimensional features is designed in this paper. It is an event-based method, which can work seamlessly with smart meter infrastructure without installing additional acquisition devices. Considering the practical application of this study is to provide appliance-level information for DR and energy-saving service, the types of the monitored load can be narrowed down to some controllable and high-power loads. This work formalizes a load identification technique based on the multi-feature integrated classification (MFIC), where the only input is the time-stamped power readings from the smart meter. The major contributions of this paper are as follows:  
1. Considering the different operating habits and inherent electrical characteristics of loads, multidimensional features are used to model each appliance and improve the load discrimination. In addition, due to the great difference of appliances signatures in different households, this paper uses proprietary model database to replace the uniform feature database. 2. Based on steady-state segmentation, a designed event detector in this paper has fewer parameters and no dependence with the detection window. 3. A linear discriminant classifier for each appliance is designed according to the overall similarity of multi-features. Based on the designed discriminant classifiers, a discriminant classifier group can be formed. The structure of this paper is given as follows. Section 2 selects multidimensional features for load modeling.