## 2 Methodology and Approach Presented  
### Methodology  
Among the various methods of data mining science, such as [42], the Cross-Industry Standard Process for Data Mining (CRISP-DM) has been the one most widely used in data mining science. CRISP-DM is a global standard in project applications in data mining. This methodology consists of 6 phases, starting with the business understanding (problem definition) phase. The data understanding and the data preparation phases are done next. To achieve a basic understanding of the data, a cleaning and preparation of the data for modeling usage is done in these two phases. The modeling phase includes various techniques to analyze data and extract knowledge. The evaluation phase and then the deployment phase are the other phases of the CRISP-DM methodology [43]. In this methodology, phases could backtrack to previous phases. The SPSS Modeler of IMB [44] has been implemented with various tools and algorithms based on the CRISP-DM, The Clementine 12.0 released in Jan 2008 and IBM SPSS Modeler 18.0 released in March 2016 [45], software of IBM has been used to perform the data mining process. Figure 1 shows the article methodology based on CRISP-DM. The article subject is defined in the first phase and it mainly discusses the problem definition. As expressed, the purpose is to identify properties affecting efficiency and also energy consumption in residential homes and also find out how to manage attributes and characteristics to achieve better energy management. An understanding and preparation of the data is done in phase 2. A sample of 49,815 records of the housing stock of England and Wales has been selected. The Department of Energy and Climate Change has published this dataset [46]. Each record represented a region, a property age, a property type, the electricity and gas annual consumption from 2005 to 2012, the floor area band, etc. Table 1 describes the data set variables. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Value** & **Description** \\ \hline HH\_ID & 1 to 49815 & Household identifier. \\  & (E12000001) North-East & \\  & (E1200002) North-West & \\  & (E12000003) Yorkshire and the Humber & \\  & (E12000004) Mid-East & \\ REGION & (E1200005) Mid-West & Former Government Office Regions (GORs) in England, \\  & (E12000006) East England & and Wales. \\  & (E12000007) London & \\  & (E12000008) South-East & \\  & (E1200009) South-West & \\  & (W99999999) Wales & Index of multiple deprivations 2010 for England. \\ IMD\_ENG & 1 to 5 & Households are allocated to five groups. (1) The least deprived and (5) the most deprived. \\ IMD\_WALES & 1 to 5 & Welsh Index of multiple deprivations 2011. This has five groups. (1) The most deprived and (5) the least deprived. \\ GconsYEAR & & Annual gas consumption based on kWh. \\ GconsYEARValid & & Flag of households’ gas consumption. \\  & & Valid gas consumption (V), households off the gas network (Q) and invalid consumption. \\ EconsYEAR & & Annual electricity consumption based on kWh. \\ EconsYEARValid & & Flag indicating households with a valid electricity consumption. \\  & & consumption. \\  & & \\ E7 Flag2012 &
\begin{tabular}{c} meters in 2012. \\ (0) Households without Economy 7 \\ electricity meters in 2012. \\ (1) gas \\ (2) other \\ \end{tabular} & Economy 7 electricity meters. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Of variables. The data is processed in phase 3. Discrepancy detection is done in this phase and there is also a negative impact on data quality which should be identified and resolved. The variable is O in 226 record values of the GconsYEARValid, which means that the household has not a gas network, while the values of the MAIN_HEAT_FUEL variable is 1, which means that the main heating fuel is gas. The records have been deleted due to a contradiction of the information.