Mapping Buildings’ Energy-Related Features at Urban Level toward Energy Planning

Abstract

To boost energy efficiency in the building sector at urban and district scales, the use of a Geographic Information System (GIS) for data collection and energy spatial analysis is relevant. As highlighted in many studies on this topic reported in literature, the correlation among available databases is complex due to the different levels of information. As the first part of a wide research aimed at estimating the energy demand of urban buildings, we present in this article a focus on the details of the GIS-based procedure developed to assess the main energy-related features of existing building stocks. The procedure is based on the elaboration of data from the Italian Topographic Databases, under provision at the national level according to the INSPIRE European Directive and the national General Census of Population and Houses. It enables one to calculate and map the urban built volume characterized by mostly diffuse use categories in an urban context (residential and offices), to which different equipment and building usage patterns can be associated, and by construction periods, featuring different technological solutions. The method has been applied to the city of Milan (Italy). An insight into the outcomes from the overall method of the wider research is also reported.

The Process of Digitalization of the Urban Environment for the Development of Sustainable and Circular Cities: A Case Study of Bologna, Italy

“…By studying and reading, with a critical eye, several contributions [22][23][24]31,[51][52][53], a methodology based on the large-scale LCA method and the bottom-up approach that can be implemented with GIS software was explored to assess different intervention scenarios (retrofit and demolition with reconstruction) on the building stock. Results can be used to produce predictive simulations and verify the achievement of emission reduction targets [24,26].…”

“…Numerous studies have recently investigated the GIS-based methodologies, aiming to characterize the existing building stock to measure its impact in terms of energy and GHG emissions in the life cycle. As such, through the elaboration of georeferenced databases and the creation of conceptual energy building models, it is, thus, possible to estimate the energy demand profiles, e.g., in Milan (Italy), and to then map these results on the territory in an effort to support energy planning [22,23]. Additionally, a methodology to integrate large-scale LCA was conducted on Esch-Sur-Alzette (Luxemburg) existing building stock in GIS software: it allowed for the production of thematic mapping and directly linked the information about the impact (GHG emissions) on the territory [24].…”

A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies

“…After having imported the mentioned data in the GIS tool (A1), once the selection of the urban area of interest (A2), dataset cleaning (A3), and related spatial correlation of the different datasets has been completed (A4), according to the detailed description in [28], the characterization of the considered building stock can be accomplished as described in the following subsection.…”

“…The city of Milan was the first case study context in which the procedure has been applied because it can be considered as representative of different urban contexts due to a mixed consistency of buildings and a variable building density (the ratio of built volume-to-land surface per each Census Unit ranges from 0.02 m 3 /m 2 to 163 m 3 /m 2 ) [28].…”

Simplified Multi-Life Cycle Assessment at the Urban Block Scale: GIS-Based Comparative Methodology for Evaluating Energy Efficiency Solutions

“…This approach was used in Esch-Sur-Alzette (Luxembourg) to create a thematic mapping of greenhouse gas emissions [25]. In Milan, a georeferenced database was created based on the development of conceptual building models [26,27]. In Helsinki, a three-dimensional predictional city model containing information on usage, materials, and energy consumption was elaborated to evaluate associated greenhouse gas emission reductions by 2050 [28,29].…”