# Optimal Strategy to Select Load Identification Features by Using a Particle Resampling Algorithm
## 1 Introduction  
Recently, non-intrusive load monitoring (NILM) has gained major attention in the research field of smart grid [1], which aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. Since this technology was invented by [PERSON] et al. [2] in the early 1980s, non-intrusive load monitoring has been considered as a low-cost alternative to attaching individual monitors on each appliance, in contrast to the intrusive load monitoring method. In addition, it can provide information, such as energy usage, the state of the device, and so on. Up to now, the technology of NILM is becoming the state-of-the-art in the field of smart grid, which is built on signal processing, pattern recognition, and such deep learning algorithms for recognizing the power consumed in a household.  
In general, the existing NILM methods can be divided into two main categories: event-based and non-event based [3]. The event-based approaches attempt to detect the changes of the state according to the significant change in power and then the features are obtained according to the changes in states. The recognition of appliances is then carried out by using a method, like the matching method and the clustering method, which is modeled by the real appliance recorded in the database. Non-event based NILM approaches are related to the machine learning from the big data collected by the single appliance or mixture of appliances during their working. However, the fundamental principle of load identification is dependent on the extraction of a stable and reliable load feature.  
Load feature, also named load signature, is often represented during the appliance's work, including the start, running, and the stop state. Generally speaking, the feature of the start state and the stop state is the most remarkable during the extraction of the load feature. In the literature,the feature extraction stage can judge the algorithm type of the appliance, such as the transient state features-based algorithms and the steady state features-based algorithms [4; 5; 6]. [PERSON] and [PERSON] [7] adopted the wavelet transform coefficients (WTCs) to get the turn-on/off transient signal identification of load events. Similarly, a multi-resolution S-transform-based transient feature extraction scheme was proposed and presented in [8]. However, transient state features were obtained only if the sampling frequency exceeded 1000 Hz [9]. Taking into account the implementation, the steady state features-based algorithms seem to be more economical than the transient state features-based algorithms. [PERSON] and [PERSON] proposed a feature extraction method in [10] by using a modified mean shift algorithm at a low sampling rate. In [11], [PERSON]'s team shared a modified version of the chi-square goodness-of-fit test for event detection and getting the load features. Moreover, there are also many other methods based on stable load characteristics [12].  
Those works above, however, almost all focused on the local information of the load during its turning on or off. The features extracted by the existing methods might not reach satisfactory predictive performance for load identification. Hence, in this paper, a particle resampling algorithm is proposed to select the desired load identification features, and then using the fuzzy theory, the method is tested with real data. The results showed that the proposed method that employed the steady state of load signatures, such as active power and reactive power, can identify the load accurately during load disaggregation.  
The rest of the paper is organized as follows: Section 2 provides the proposed method, Section 3 describes the NILM platform in this work and the experimental results, and, finally, Section 4 concludes the paper.