## 3 Modeling and Analyzing the Energy Efficiency without the Proposed Approach  
As mentioned, the purpose of the paper is to discover the factors affecting energy efficiency and consumption and also to assess the proposed approach. So, the properties are modeled once without using the approach and then using it to analyze the energy efficiency in the domestic sector. The energy efficiency rate for each record obtained from EPCs logged for dwelling. The EPCs gather information on physical characteristics of the property and the main heating fuel, and gives score based on standard assumptions about residents and behavior. Then quantifies a dwelling's performance in terms of an efficiency rating (A the most efficient and G the less efficient). In this data set the most records' energy efficiency band is D (42.92%) and fewer records are in band A, B (2.71%) and G (1.31%). This section deals with modeling and assessing the impact of properties on energy efficiency without the proposed approach. The aim of using decision trees is to obtain the most effective factor target class for each case in the data. For this purpose, the C5.0 algorithm [47] is used and all the variables except energy consumption are employed as the input, the energy efficiency group being the target. It can be said that the biggest advantage of C5.0 is that it presents its classification model as a tree structure which can be easily interpreted as rules. An advanced classifier may have better accuracy in many datasets but they cannot be easily understood and visualized. Also, the C5.0 reduces the pruning errors and has the ability of feature selection [48]. In C5.0, the root node is the most important variable and the best predictor. The leaf nodes contain a class label of the classification target. The percentage presented in the tables show Ptrgt/Prule, which are:  
* Ptrgt: Percentage of records that have the characteristics of the relevant rule and are also in the target energy efficiency group. * Prule: Percentage of records that have the characteristics of the relevant rule. Table 2 includes the results of analysis using the C5.0 algorithm. In this table, the rules corresponding to the C5.0 tree branches, which have a significant difference in the percentage of the target class, are presented in no particular order. It can be said that houses, which have a large share of better energy efficiency groups ((A, B) or C) are good cases for improving energy efficiency in other homes with similar attributes. Figure 2: An overview of the proposed approach. According to the rules, some knowledge can be discovered. * Carefully scrutinizing the rules of rows 2 to 5, it is concluded that installing a boiler will result in better energy efficiency. Dwelling which install boilers in 2009 are in better energy efficiency group. They are followed by the boilers installed in 2005 and 2012. The homes whose boiler was installed in 2010 have not a good Energy Efficiency. * A comparison of rules 8 and 9 indicates that in area 1, tariffs do not yield an improved energy efficiency. Of course, this was seen among other households, but was not provided due to the low support value. * In the rules of rows 1, 11 and 13, the household energy efficiency group is very bad. The issue is their built year (built before 1930). Obviously, in old houses, the thermal performance and energy consumption of equipment are weak compared to new ones. In general, only 0.35% of the old homes of datasets have energy efficiency groups A and B, while nearly 52% of them are in weak energy efficiency groups (E, F, and G). But in newly-built houses (after 1996), these percentages reach 13.8% for the energy efficiency groups A and B, and only 2% for poor energy efficiency groups. * Rules 1, 10 and 11 refer to similar old houses that are located in different regions. Among these rules, the homes of Wales are in better condition in terms of energy efficiency.