# A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data
## 1 Introduction
The level of electricity consumption in Singapore was also analyzed in this paper. [PERSON] et al. [12,13] analyzed the effects of residential electricity prices, per capita GDP, population, cooling degree days, and rainfall on electricity consumption by Chinese residents. [PERSON] et al. [14] considered different energy-saving measures--such as using insulation materials, changing lighting devices and refrigerators to reduce power consumption, and using renewable energy such as photovoltaics--and used multi-objective genetic algorithms to solve optimization problems in order to minimize Portugal's residential energy consumption. [PERSON] et al. [15] collected information on residential building features, energy use, and user's income to assess energy efficiency. The evaluation results showed that by replacing air conditioners, users can reduce their electricity consumption by 32% annually, and income level is proportional to electricity consumption. In summer, the housing area is proportional to the electricity consumption. [PERSON] et al. [16] and [PERSON] et al. [17] studied factors that affect household energy efficiency other than household appliances, such as resident age, house ownership, and energy-saving rebound effects. As an essential element of modernizing power grids to form future smart grids, distributed generations (DGs) have developed rapidly in recent years [18]. [PERSON] et al. [19] proposed a bottom-up method to study the random behavior of power users, which took into account the number of residents and the use of different equipment to get the behavior of residents. The evolution of smart distribution grids is driven by the rapid growth of distributed energy resources (DERs) [20]. The integration of DERs, such as DGs, distributed storage (DS), demand response (DR), electric vehicles (EVs), and microgrids (MGs), makes the distribution system more active [21]. [PERSON] et al. [22] summarized the existing energy efficiency assessment methods and tools. In their review, the assessment objects were mainly industrial systems, and the difficulty of energy data collection resulting in a lack of data was identified as a recurring problem. Thus, increasing the availability of data is important for improving the accuracy of data. Non-intrusive load monitoring (NILM) can be used to solve the problem of insufficient data, which can, in turn, effectively solve various problems caused by a lack of data and improve the accuracy of an energy efficiency assessment. Many researchers have analyzed the energy efficiency level of industrial users, but there are few papers on the energy efficiency assessment of ordinary residents. Moreover, some indicators are difficult to obtain, or it is difficult to assess their impacts on residential energy efficiency. It is difficult to evaluate the power consumption characteristics of users due to insufficient information and large data granularity, which is the main problem addressed in this paper. With NILM data, the user's power consumption information is integrated, and the energy efficiency analysis is performed. The power consumption information includes the user's hourly power consumption, the hourly power consumption of household appliances, and the total power consumption. Through this information, this paper explores an energy efficiency assessment of residential users, constructs a family energy efficiency indicator system, and then proposes a household energy efficiency assessment method based on NILM data. This method can reflect upon the impacts of household population, area, and income on users' electricity consumption; classify users according to user feature; unite the entropy weight method with the solution distance method to evaluate users' energy efficiency; and finally give users suggestions through the target method. Compared with existing methods, this method has the following features:  
(1) It can build a user portrait label system by studying the portrayal of each user.