The recent focus on new strategies towards the achievement of smart cities and energy community goals has required a massive use of urban scale tools for building energy modelling. The main aim is to support decision makers to address urban energy policies allowing the development of energy scenarios combining multiple actions. Despite some exceptions of simplified input datasets, urban scale simulation tools commonly require a large amount of input data to describe the building stock investigated, depending on the tool and the modelling purpose. Several literature studies explained the building stock modelling challenge, enhancing the current lack of complete databases describing the national building stock. The regional datasets of energy performance certificates are not fit for purpose and are often not available for research or statistical analysis. To tackle this issue, a hybrid approach combining different sources of information can be implemented; however, large quantities of data belonging to heterogeneous datasets must be updated, harmonized, integrated, potentially reducing the available data or reducing the accuracy. For these reasons, a possible way is the definition of archetype and prototype buildings, defined as ideal buildings described by sets of characteristics considered as representative of certain clusters of the building stock. However, a major challenge still must be solved: how is it possible to properly distribute archetype properties respecting the real presence of buildings within the considered location? In this work the last Italian population and housing census has been used to determine the distribution of building typologies according to the Italian building stock. Statistical analysis allowed for the clustering of the available information to deal with the lack of information for urban scale modelling tools, providing useful data for the representativity of available information within the national building stock. Future applications will apply the methodology to other case studies.
The challenge of archetypes representativity for wide scale building investigation in Italy
Zarrella A.
2025
Abstract
The recent focus on new strategies towards the achievement of smart cities and energy community goals has required a massive use of urban scale tools for building energy modelling. The main aim is to support decision makers to address urban energy policies allowing the development of energy scenarios combining multiple actions. Despite some exceptions of simplified input datasets, urban scale simulation tools commonly require a large amount of input data to describe the building stock investigated, depending on the tool and the modelling purpose. Several literature studies explained the building stock modelling challenge, enhancing the current lack of complete databases describing the national building stock. The regional datasets of energy performance certificates are not fit for purpose and are often not available for research or statistical analysis. To tackle this issue, a hybrid approach combining different sources of information can be implemented; however, large quantities of data belonging to heterogeneous datasets must be updated, harmonized, integrated, potentially reducing the available data or reducing the accuracy. For these reasons, a possible way is the definition of archetype and prototype buildings, defined as ideal buildings described by sets of characteristics considered as representative of certain clusters of the building stock. However, a major challenge still must be solved: how is it possible to properly distribute archetype properties respecting the real presence of buildings within the considered location? In this work the last Italian population and housing census has been used to determine the distribution of building typologies according to the Italian building stock. Statistical analysis allowed for the clustering of the available information to deal with the lack of information for urban scale modelling tools, providing useful data for the representativity of available information within the national building stock. Future applications will apply the methodology to other case studies.
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