## 1 Introduction
(2005) [11] conducted a more comprehensive study of the energy usage of 193 residential building stocks from five European nations. The Danish residential building stock was studied by [PERSON] and [PERSON] in 2006 [12]. They were referring to two different types of structures: a single-family house and a multi-family one. [PERSON] and [PERSON] (2009) [13] investigated European (EU27) residential building stocks, offering a model to depict the evolution of the respective building stocks for each of the nations. Three types of buildings have been recognized in further detail: single-family, multi-family, and high-rise. From 1900 until 2060, these classifications were further separated into historical and modern buildings. The energy performance of residential building stocks in two big Italian regions, Piedmont and Lombardy, was studied by [PERSON] and [PERSON] in 2011 [14]. The study was conducted using data from the Italian census, which included 72 distinct building geometries, four different construction age categories, eleven different heating system efficiencies, and a variable number of degree-days (DD) categories with a predetermined step of 100 DD. [PERSON] et al. (2015) [15] established an approach for defining RBs in Portugal that suit the requirements and address the information gap. This strategy was successfully adopted in order to identify an RB (family home) that was typical of residential structures built in Lisbon between 1961 and 1990. In this study, building function type, building location, and age of construction were prioritized over other criteria used by existing works. In Florianopolis, Brazil, [PERSON] and [PERSON] (2016) [16] devised a strategy for generating RBs for low-income residential properties. Field data were collected in terms of creating a database of housing geometrical characteristics. Cluster analysis yielded two RBs: a 76-square-meter home with a living room, kitchen, and three bedrooms; and a 37-square-meter house with an integrated living room and kitchen, and two bedrooms. Simulations have demonstrated that the RBs can accurately reflect their cluster, with degree-hour values that are comparable to the housing median sample. A comprehensive survey was conducted by the Buildings Performance Institute Europe (BPIE) throughout all EU Member States [17]. This study gives an overview of the building stock in Europe. Building typology (function type), building age, building size, and building location were all factors in BPIE's survey. This categorization relates to the statistical data supplied by the EU nations that took part in the survey. Furthermore, many European projects, such as TABULA [18], ASIEPI [19], and IMPRO-Building [20], have focused on the identification of RBs, either for promoting energy-efficient guidelines or validating tools and measures for energy performance requirements or facilitating cross-country comparison of building stocks. According to [PERSON] et al. (2011) [21], the Department of Energy (DOE) produced 16 RB models that characterize more than 60% of the commercial building portfolio in the United States. These models are intended to portray building attributes and construction techniques in a realistic manner. They were divided into three construction eras and consisted of 15 business buildings and one multi-family residential structure (pre-1980, post-1980, and new buildings). The information gathered for the creation of RBs can be divided into four categories that comprise a broader range of features: building type and general geometry, construction technologies and materials, HVAC systems and renewable energy production, and operational parameters. The study was conducted to evaluate new technologies, optimize designs, analyze advanced controls, produce energy codes and standards, and perform lighting, daylighting, ventilation, and indoor air quality research. More recently, the research of [PERSON] et al. (2018) [22] proposed a building clustering methodology based on satellite images from Yuzhong district, China.