Statistical GIS-based analysis of energy consumption for residential buildings in Turin (IT)

Greenhouse gas emission is an important issue and the largest source of it is from human activities and from building sectors. Therefore, the building stocks play a key role in the reduction of GHG emissions through the analysis of the energy performance of buildings, in order to understand their behavior and to identify effective models that will allow expanding investigations in vast areas as districts or cities. This work analyses space heating energy performance of buildings with a multi-scale approach using the main energy related variables at building, block of buildings and district scale. The purpose of this study is to identify a simple regression model in order to evaluate the space heating energy consumption of a large part of residential buildings in Turin (IT). A cluster analysis was applied in order to find groups of buildings with similar energy consumptions and to identify the main energy-related characteristics of each group. The analysis was developed with the support of a GIS tool to evaluate the buildings characteristics and a statistical software to identify a stable model at urban scale. The identified models evidenced that the space heating energy consumption not only depends on the characteristics of the building itself, but also on the urban characteristics. At urban scale, the most influential variables were: the heating degree days, positively correlated with the space heating consumption, and the albedo that was negatively correlated. Also, socio-economic variables were utilized: the percentage of working people with a positive correlation and the percentage of young inhabitants with a negative correlation. The statistical GIS-based methodology proposed in this study is simple and then replicable to other urban contexts. This kind of analysis can be useful for policy makers in defining specific energy efficiency measures for each group of buildings to identify new more effective energy performance variables and benchmarks for the different groups of buildings and then to improve the energy performance of a city reducing energy consumptions and the relative GHG emissions