“…In the graphs, the value 0 indicates that the internal air temperature of the building was set at 20°C in winter and 28°C in summer, while the value 1 indicates that the internal air temperature was set at 22°C and 26°C in winter and summer, respectively. This means that the energy system was always in operation to keep the building temperature at 22°C or 20°C during the heating season and at 26°C or 28°C during the summer season, according to the literature ( Tardioli et al, 2020 ) and as required by the SIA 2024 Swiss norm ( Zurich, 2006 ). Figure 6 Occupancy schedules of the baseline scenario (S1): (a) weekday, (b) weekend.…”
“…In the graphs, the value 0 indicates that the internal air temperature of the building was set at 20°C in winter and 28°C in summer, while the value 1 indicates that the internal air temperature was set at 22°C and 26°C in winter and summer, respectively. This means that the energy system was always in operation to keep the building temperature at 22°C or 20°C during the heating season and at 26°C or 28°C during the summer season, according to the literature ( Tardioli et al, 2020 ) and as required by the SIA 2024 Swiss norm ( Zurich, 2006 ). Figure 6 Occupancy schedules of the baseline scenario (S1): (a) weekday, (b) weekend.…”
“…Thus, access to reliable and homogenous statistical data regarding building stock-related information is a complex procedure that restrains developments in the UBEM sector. This is mainly related to the integration and availability of large data sources, privacy protection schemes, and extreme variations in building data sources from country to country (Tardioli, et al, 2020). Connecting building footprints or 3D building map objects with building-physics related information reflects one of the most established methods for developing bottom-up UBEM tools.…”
The European Union has set strict goals towards climate-neutral cities by 2050, pushing private and public stakeholders to develop integrated assessment tools for measuring urban buildings’ energy performance and designing sustainable retrofitting strategies. Within this context, efficient energy use modelling of urban built environments, in a precise and transparent manner, is a crucial driver for global cities’ decarbonisation. Thus, rapid developments of bottom-up and top-down methodologies and tools reflect the current international research and technological developments in the Information and Communications Technology (ICT) software industry. Combined bottom-up based GIS technologies and building typologies (archetype buildings) enable a detailed energy performance analysis of urban built environments. Since the results are produced for each building unit, these methods offer scalability, starting from single buildings to neighbourhoods and whole cities. In order to assess the energy performance of urban buildings in a bottom-up and efficient way, several aspects must be considered, such as substantial differentiations in archetype buildings according to the use, location, year of construction and each country’s profile. Furthermore, access to pan-European building typologies data, developed with the same methodology, is crucial to overcome such barriers. Moreover, although dispersed methodological tools already exist, no integrated methods have yet been developed, that can be easily used on a pan-European base. As an innovative, cloud-based tool, the Re-Polis platform aims to fill the gap in urban buildings’ large-scale energy efficiency modelling. Within this context, this paper describes all barriers identified during the design of the Re-Polis platform, regarding the development of an end-to-end robust methodological procedure and the decisions that were made to overcome them.
“…Clustering, not imposing particular weights on variables, enables for a more accurate and unbiased building stock segmentation of the building stock compared to traditional classification procedures producing an unbiased grouping outcome (Tardioli et al 2020). This method is generally implied for three main purposes: a) to identify underlying structure, b) to conduct natural classification based on the degree of resemblance, c) to do compression for summarizing data based on cluster prototypes (Jain, 2010).…”
Section: Fig 1 Co-occurrence Network Of Abstract In the Building Retr...mentioning
EU building sector consists mainly of outdated and inefficient properties with high energy consumption. Hence, building retrofit is being emphasized as a feasible alternative for addressing existing challenges, taking lots of time, effort, resources, and expertise in its traditional form. Conventional case-based retrofit scenarios fail to deliver quick and objective solutions for massive datasets. This research benefits from Artificial Intelligence, particularly clustering techniques, to enhance strategic decision-making for building retrofit and solve the shortcomings of conventional methods. It connects the dispersed Italian databases (CENED and TABULA) and determines desired building technology and retrofit strategy to obtain an optimum energy label.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
