# A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data
## 3 Household Energy Efficiency Assessment Method Based on NILM Data
### Household Energy Efficiency Evaluation  
TOPSIS and visekwriterijumsko kompromisno rangiranje (VIKOR) [30] are two kinds of decision-making methods, but the latter requires a criterion weight coefficient and criterion value to be determined, which is difficult to achieve in practical decision-making. However, the TOPSIS method has become mature, and many decision-making problems are solved by TOPSIS. Therefore, this paper adopts a more mature TOPSIS method for comprehensive evaluation.  
The current mainstream methods for determining index weights are analytic hierarchy process (AHP) and entropy. However, AHP is greatly influenced by subjectivity; different experts have different opinions on the weights of the indicators, and different households have different characteristics of electricity consumption for household users. If AHP is adopted, its subjective nature will lead to large errors. The entropy method is more objective, depending on the data itself.  
The evaluation system has an evaluation index system, adopts k-means to cluster users, classifies users into five different types according to family information A in Figure 4, and adopts the entropy value method to weight the indicators. Finally, TOPSIS is used to conduct a comprehensive evaluation of users so as to obtain corresponding evaluation scores. According to the result of clustering, different types of users or users of the same type are classified and calibrated. This approach is an effective way to obtain the weak links of energy use in households and give corresponding suggestions by comparing the differences in energy use among the different households.  
In this paper, the data is preprocessed to delete abnormal and invalid data. Because the existing data in this paper is limited, Monte Carlo simulation is used to obtain more data. Monte Carlo is a calculation algorithm that relies on repeated random sampling [31]. It approximates the expected result of the total data by simulating a large new sample based on the real data.  
Most homes have a certain number of electrical appliances, such as a refrigerator, washing machine, and air conditioner, which need to be uniformly processed and combined according to Equation (2) for assessment.  
\[P_{i}=\sum\limits_{j=1}^{n}p_{j}, \tag{2}\]  
where \(P_{i}\) is the power of the \(i\)-type appliance and \(P_{i}\) is the power of the same type of appliance.  
The use of new energy may not reduce energy consumption, but for household expenditure, environmental protection, and other aspects, new energy is the best power choice. This paper quantifies the new energy utilization efficiency to calculate the energy ratio of new energy, which is the ratio of energy consumption of new energy output to total energy consumption of the family, shown in Equation (3):  
\[\eta=\frac{\sum\limits_{k=0}^{m}P_{k}}{P_{\Sigma}}, \tag{3}\]  
where \(P_{k}\) is the energy consumption of a new energy supply system (e.g., photovoltaic or wind power). When \(k=0\), it means that the family is without DGs, and \(P_{\Sigma}\) is the total energy consumption of the household.  
According to the power characteristics [32] above, the user is classified by K-Means [33], and similar users use the entropy weight method and TOPSIS [34; 35; 36; 37] to evaluate the energy efficiency of the users.