# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 1 Introduction  
Load disaggregation technology is a key technology in smart grids [1]. Traditional load monitoring adopts intrusive methods, which are able to obtain accurate and reliable data with low data noise [2], but they are difficult to be accepted by users due to their high implementation costs. Non-intrusive methods can provide detailed information for residents in time, and have the advantages of low cost and easy implementation. According to this technology, the power consumption behaviors of users can be analyzed, and users can be guided toward a reasonable consumption of electricity and hence reduce their power consumption costs. With the continuous development of power demand side management [3], big data analysis, and other technologies, non-intrusive load disaggregation is attracting more attention. The microgrid is an important manifestations of the smart grid. With the development of clean energy, such as solar and wind energy, and energy internet technology, the microgrid has emerged. It is a small power system with distributed power sources, which can realize a highly reliable supply of multiple energy sources and improve the quality of the power supply [4]. As NILM technology becomes more mature, the intelligent dispatching of the microgrid can be realized through automation in the future to improve the effective utilization of power resources, ensure the stable economic operation of a power system, and avoid the unnecessary waste of power resources. Therefore, NILM technology is important. The concept of non-intrusive load monitoring was firstly proposed by [PERSON] [5]. It mainly uses non-intrusive load disaggregation (NILD). In this method, the total power consumption is disaggregated to each individual electrical appliance. [PERSON] proposed the concept of \"load characteristics\", which he defined as the information change of the electrical power of an appliance in operation. [PERSON] further used the steady-state load characteristics [6] to design a simple NILD system to decompose power. However, effective features extracted by the algorithm were limited, and large disaggregation errors occurred easily. At present, combinatorial optimization (CO) methods and pattern recognition algorithms are the main algorithms for realizing non-intrusive load disaggregation. Among them, NILD based on a combinatorial optimization algorithm [7] determines the power consumption value of each appliance by investigating load characteristics as well as error comparisons between power states of combined appliances and the total power. [PERSON] [8] and [PERSON] [9] used the Particle Swarm Optimization (PSO) algorithm to solve the disaggregation problem based on the steady state current on a few electrical appliances, but the disaggregation result error was large. In order to solve the NILD problem, [PERSON] [10] proposed a sparse optimization method to improve the disaggregation accuracy. The combinatorial optimization method is essentially an NP-hard problem, so its efficiency is a challenge. In addition, the optimization theory could be only used to analyze discrete states of electrical appliances, so it is difficult to model loads with large load fluctuations. With the development of machine learning, pattern recognition algorithms have been applied to NILD. [PERSON] [11] solved the depolymerization problem of low-power appliances using K-nearest neighbor regression (KNN) [12], but the algorithm could not solve the problem of the large power difference between appliances. [PERSON] [13] used the sparse coding algorithm to learn the power consumption models of each electrical appliance and used these models to predict the power of each electrical appliance. [PERSON] [14] used unsupervised learning for NILD, and this model had a high training speed. However, compared to the supervised algorithms, the ability of [PERSON]'s method's to identify complex continuous state loads was limited because of the lack of prior knowledge.