# A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data
## 4 Case Study  
The current experimental data is the daily electricity consumption data of 20 users within 30 days, including the electricity consumption information of about 51 different electrical appliances obtained by NILM. The family population, income, area, and other information are obtained randomly in the text by the simulation method. Among them, the population range is (1, 5), family income range is (10,000, 30,000) yuan, and house size range is (70, 120) square meters. Therefore, the relevant family information can be obtained through user surveys in practical applications. The example in this paper is completed on a personal computer with a CPU of i3-3.90 GHz and a memory of 12 GB. Meanwhile, this paper uses MATLAB for algorithm simulation, and all experimental data are processed by MATLAB (R2018a, MathWorks, Tianjin, China, 2018).  
The energy efficiency evaluation model is an online operation mode, and the evaluation interval is usually 7 to 30 days. The sampling period of the experimental data in this article is 15 min, which corresponds to the 96-point load data in one day. At present, NILM equipment can detect the usage of 51 different electrical appliances, and the installation of the equipment is still in the pilot stage. Therefore, data on 20 users recorded for more than a year were selected, and 30 days' worth of data were selected as the experimental data. An energy efficiency assessment can be regarded as a \"value-added service\" provided by the grid company. To ensure the integrity of the user survey data, pilot users need to fill out user survey reports under the guidance of the grid company.  
Active power and reactive power are the most commonly used steady-state electrical characteristics in non-invasive load decomposition. To some extent, the usage of load can reflect the user's electricity consumption behavior, while the probability distribution of the load's state behavior can provide key characteristic information. In Figure 6, the active power scatter diagram is drawn according to the obtained data of six typical loads for two weeks, with a period of 24 h to show the electrical characteristics and behavior rules of each load. The darker the scatter color, the greater the number of superimposed data points.  
As can be seen from Figure 6, there are certain differences in the power characteristics of household load, which can be used to realize non-invasive load decomposition. Different functions and usage habits of the load equipment reflect the unique state behavior rules in each time period throughout the day. For example, dishwashers are typically used before or after meals, and the TV is often turned on in the afternoon or evening.  
Figure 7 shows a load decomposition diagram of different types of appliances. The sample period of the electrical data collected during the characteristic analysis of the electrical appliances in the NILM equipment is 1 s. In (a), six types of refrigerators have various load features, and (b) shows the power levels of different types of microwave ovens. Thus, in accordance with the characteristics of load power, it is essential to improve the accuracy of the energy efficiency assessment by fully applying the time characteristics of the equipment and user habits.  
Figure 6: Time distribution of typical load power.  
Figure 7: Power decomposition of different types of electrical appliances. (**a**) Refrigerator; (**b**) Microwave oven.