## 1 Introduction  
In today's world, supplying energy is done through various carriers such as oil and gas (and products derived from them), electricity and renewable energy. Given the limited resources of energy and the population growth, the increasing annual consumption of energy affects life, the economy, the environment, politics, and so on. So, managing energy is a complicated task and has become an important issue in the modern world. The home section has the largest share of energy consumption in most countries. As each house has its own behavior, energy consumption patterns rely on several factors. Hence, decision making concerning the domestic sector's energy management and efficiency requires taking advantage of modern science capabilities to manage energy efficiency and consumption. Data mining science can extract useful knowledge which is hidden in the data. Using its methods and algorithms, data mining techniques analyze huge amounts of data automatically [1,2]. In general, data mining is the process of Knowledge Discovery in Databases (KDD). Knowledge obtained from this fashionable science can be verified by conventional analysis [3]. As this science proves its potential in solving versatile problems [4], using it to extract knowledge in domestic buildings for managing energy is reasonable. Scholars aim to develop innovation changes in the field of energy, so many studies have been conducted on the use of data mining science in energy management and efficiency. Energy performance certificates (EPCs) measure energy performance and give recommendations on how to improve energy efficiency [5; 6]. They try to find inefficient building properties and improve them. In other words, they help to locate properties whose energy performance is effective and better them to improve their energy efficiency [7]. [PERSON] et al. review existing applications of EPCs and present a method of EPC data quality assurance using data analytics [8]. Developments on EPCs have been done using data mining in recent years [9; 10]. Some important insights related to the energy management requirement for buildings to save energy have been presented in [11; 12]. Also, data mining tasks that can be used to mine building-related data have been shown in [13]. There are studies which focus on discovering factors which influence energy. [PERSON] analyzed the impact of psychological, family and contextual aspects on residential energy consumption which indicate saving money, energy concern, and behavioral barriers which have a major impact on residential energy consumption behavior [14]. Effective factors on home electrical energy demand have been analyzed through developing a model using series prediction methods. The result demonstrates that houses using pool pumps and ducted air-conditioning have an increased electricity consumption, whereas houses with gas hot water systems have a lower power consumption than homes that do not use these systems [15]. The adaptive neuro-fuzzy inference system (ANFIS) has been used to discover major factors influencing energy consumption. This indicates that insulating materials are the most important parameters in building energy consumption. Attributes such as the type of materials and their thickness, wall structures, roofs and their ability to stay hot or cold, the location of walls and windows and geographic area have a major impact on energy saving [16]. The use of unsupervised learning has been applied to discover electricity consumption patterns in a Spanish public university. The authors found different clusters in which several buildings were identified. Such clusters were interpreted and rules for saving energy were proposed [17]. Clustering data is the process of putting data in a group so that they have the greatest similarity and are very dissimilar from data from other clusters. Many studies have been done in clustering [18; 19; 20; 21]. Clustering is also used in the field of energy.