In particular, by identifying clusters of Individuals, households, organizations, cities, countries and nationalities with similar behavioural patterns, it can assist in the crafting of more effective interventions and incentives targeted to specific energy cultures. it also helps energy supply companies understand different behavioural clusters among their customers, so as to better tailor their tariff schemes and products. The purpose of this paper is clustering of buildings by using Grey Set Theory. This theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for this study rather than traditional statistical regression which requires massive data, normal distribution in the data and few variant factors. Gray clustering in this study has been used for two purposes. First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, Grey Clustering with Variable Weights has been used to classify all buildings in three categories named “standard”, “Moderate standard deviation” and “completely non-standard”. This classification can be the basis of behavioral research on each group and understanding of cultural differences in each cluster, regardless of technological and structural differences between the buildings. In addition it can be as a tool for understanding the potentials and possibilities for sites of action to achieve behaviour change, whether these are at a general policy level, or targeted at a specific group