## 1 Introduction
The dataset has been classified into low, medium and high energy demand categories to show the factors influencing heating and hot water. A detailed analysis was performed using the k-means algorithm in the high consumption category. The output model presents good energy demand patterns and optimal ways to design buildings. The average U-value of the opaque envelope followed by the aspect ratio is the most important variable [22]. Characteristics of energy consumption examined have used cluster analysis for 134 LEED-NC certified office buildings. The buildings gathered into three clusters (low, medium and high) are very different and each one has a special attribute. The lower U-value of the roof and a lower ratio of windows are the factors which most influence a lower consumption. The HVAC system has a similar performance in all the clusters. The internal process load has a significant impact on clusters [23]. A framework based on data mining used CART classification and K-means clustering to analyze the pattern of energy consumption in a large data set of flats. Four influencing attributes (aspect ratio, U-value of vertical opaque envelope and windows, the average global efficiency of the system for space heating and DHW) were analyzed. High consumption flats were clustered and a reference flat was identified. These can be used to propose different energy retrofit actions [24]. The consumption of electricity and heating in six schools was studied using k-means clustering and self-organizing maps to evaluate energy efficiency. The schools have different construction years, areas, numbers of students and heating systems. Schools 1 and 4 have the highest cost of energy on working days and schools 2 and 3 have the lowest cost of energy at weekends compared to the others. The newest schools are generally better than the old ones in the field of energy efficiency [25]. A study of energy efficiency in 132 countries was estimated using a Data envelopment analysis model and then K-Means clustering, which specifies whether countries in a cluster are in the field of development or not. The results show that countries could develop energy efficiency by changingenergy-related indicators [26]. A cluster analysis was used to analyze the regulations on energy efficiency of buildings of South America and Europe. This showed that buildings located in a similar climate zone but in different regions (countries) have different energy performances. It indicated that the tendencies of energy performance are different between various countries' regulations and the climate zones. The results confirmed the ability of cluster analysis to highlight similarity patterns between various regions of the same climate [27]. In the field of energy management systems, ISO 50001:2011, a systematic approach to improve the energy performance, plays an important role in the energy field. A study which classified, gathered, clustered and then applied data analysis techniques showed strategic decisions for improving the energy performance. The idea is used in an oil refinery and outputs of better energy management are shown [28]. Also in [29; 30], efforts were made to develop the energy efficiency in industrial buildings. A fuzzy clustering technique was developed to rate school buildings in Greece. The methodology demonstrates that the energy consumption and global environmental quality of school buildings can be significantly improved, but the indoor air quality of these buildings causes some problems for them [31]. Wind is an energy source whose identification and assessment in its training needs is very important. The Analytic Hierarchy Process has been used to specify the training of wind farm employees. The results of the research prioritized the tasks and appropriate training courses tailored to the indicators were provided [32]. Discovering the rules is very much challenged in data mining [33; 34; 35; 36]. [PERSON] et al. present associations between building operational data.