# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 3 Overview of the Dataset
### Variable Categorization  
Machine learning models for energy consumption rely on various features such as outdoor weather conditions, building size and surface area, and dwelling typology [39]. For instance, weather conditions, such as temperature and rainfall, are considered for urban or rural areas where a dwelling is located. Similarly, building characteristics, such as the number of rooms, determine the dwelling typology.  
According to a review conducted by ref. [2], heating and air conditioning (HAC) systems, along with domestic hot water (DHW) systems, are some of the major energy-consuming appliances in households. This variation in energy consumption is attributed to differences in thermal comfort expectations, resulting in temperature adjustments, and to different degrees of importance placed on occupant behavior [40].  
According to ref. [1], age and family size are important factors linked to energy consumption. Homes with fewer children have more flexibility in their energy-consumption practices compared to homes with children, where parents have less flexibility in their routines. The number of people living in a house is also a significant factor affecting energy consumption, as energy usage tends to increase with the number of occupants [41]. Additionally, ref. [42] suggests that homes with fewer children can perform different practices and consume energy differently, making them more flexible in their energy consumption.  
Household disposable income and education level are important factors that influence energy consumption across various energy load profiles [43]. When families have higher incomes, they tend to own more appliances and thus consume more energy. However, a higher income can also lead to investments in modern energy-efficient equipment, thereby decreasing energy consumption.  
Each row in the dataset is a twelve-element array consisting of eleven characteristics and one of three cluster types. To provide a clearer understanding of the dataset, Table 2 presents the description of the characteristics, and Table 3 provides the statistical analysis of the numerical variables, including their mean, median, standard deviation, minimum and maximum values.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Feature** & **Description** \\ \hline ARE & Area in which the dwelling is located (0-Urban, 1-Countryside) \\ TYP & Dwelling typology (0-Standard house, 1-Standard apartment, 2-Duplex or triplex) \\ BED & Number of bedrooms \\ HAC & Number of heating and air conditioning units in use \\ DHW & Number of domestic hot water units in use \\ COE & Usage of electric cooktop and/or electric oven (0-Yes, 1-No) \\ WPU & Usage of electric water pump (0-Yes, 1-No) \\ NPE & Number of people living in the dwelling \\ U18 & Number of people under 18 living in the dwelling \\ PET & Presence of pets, specifically dogs and/or cats (0-Yes, 1-No) \\ INC & Value of family income (0-Up to RS 1903.98, 1-From RS 1903.99 to RS 4664.68, 2-Above R5 4664.68) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of features in the behavior dataset.