# A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data
## 4 Case Study
### Cluster Analysis  
After PCA processing and analysis of its cumulative variance contribution rate, the first three principal components contain most of the information of the original data set. Therefore, fewer principal components can be used to represent the original data set to achieve dimensionality reduction. After dimensionality reduction, k-means is used to cluster the principal components of the PCA to obtain the user clustering results as shown in Figure 8. After PCA dimensionality reduction, users are divided into five categories by k-means. Some users in the 5 th category are similar to those in the 1 st, 2 nd, and 3 rd categories, but others are quite different. The 4 th category is different from the other groups and has the fewest users. Figure 9 is the cluster center curve of the electricity data from each electrical appliance of five types of users. From the trend of the curve, it can be seen that the energy usage for all kinds of consumer electrical appliances is roughly the same. For example, there is an increasing trend for electrical appliances No. 3, No. 13, and No. 24, whereas there is low power consumption for electrical appliances No. 25 through to No. 36. It is worth noting that the power consumption by users in the 2 nd category for electrical appliances No. 6 through to No. 8 is higher than that by users in the 3 rd category or 5 th category, which can be analyzed in the subsequent energy efficiency analysis, and corresponding power consumption suggestions can be given. The description of each appliance is presented in Appendix A. The power consumption of the users in the 1 st category is mainly for electrical appliances No. 13, No. 24, and No. 49. Users in the 5 th category use more electricity for conventional appliances than other types of users. Users in the 2 nd category and 4 th category have similar characteristics of electricity consumption for conventional electrical appliances, and the electricity consumption for electrical appliance No. 48 (air conditioner) is relatively prominent. In thesubsequent energy efficiency analysis, the rationality of electricity consumption of these two types of users can be analyzed emphatically, and corresponding suggestions for electricity consumption can be given. Compared with other types of users, users in the 3 rd category consume less power for electrical appliance No. 48 (air conditioner). Table 2 shows the specific characteristics of various users, including power consumption, household population, and income. It can be seen that the average power consumption of the 3 rd category is the lowest. The power consumption levels of the 2 nd category and 5 th category are both relatively high, so it is necessary to examine the energy consumption levels of these two types of users and to give corresponding energy recommendations. Since the average energy consumption of the 3 rd category users is low, and the family population and house area in Table 2 are at the same level, such types of users can be defined as the \"business trip group\". The cluster center of the five types of users has a small gap, and the main difference lies in the user's energy consumption. The energy consumption levels of users in the 1 st category and 4 th category are similar. Users in the 4 th category have a  
Figure 8: User clustering graph after PCA dimension reduction. Figure 9: Five types of users’ electrical energy consumption clustering center curve. lower household income, while users in the 1 st category have a lower household income and lower population income level. There are five categories of users: 2 nd category--high energy consumption; 1 st category--medium and high energy consumption; 4 th category--medium energy consumption; 5 th category--medium and low energy consumption; and 3 rd category--low energy consumption.