Slide 1.pngSlide 2.pngSlide 3.pngSlide 4.pngSlide 5.pngSlide 6.png

Dr. Guglielmina Mutani

Department of Energy, Politecnico di Torino, Italy


Talk Title
Space heating energy consumption model at urban scale with a statistical approach

Talk Abstract

Nowadays climate change is an important topic that has created awareness about the imbalance that we are generating with our activities. The building sector has the largest potential for significantly reducing greenhouse gas emissions compared to other major emitting sectors. Therefore, the first step in the improvement of the energy performance of buildings is to study, understand and simulate their behavior in order to create effective models that will allow expanding investigations in a bigger area as district or even cities.

This work analyses the buildings space heating energy performance at multi-scale through the evaluation of the main energy-related variables at building, block of buildings and district scale. The purpose of this study is to identify a multiple linear regression model in order to evaluate the space heating energy consumption of a large part of residential buildings of Turin (Italy), to apply a cluster analysis in order to find buildings with similar energy consumptions and to identify the main energy-related characteristics of each group.

The analysis was developed using a GIS tool to identify the buildings characteristics and a statistical software to find the correlations between the dependent and independent variables. In particular, the statistical software SAS Enterprise Guide allows the implementation of different statistical techniques as principal components, multiple linear regressions, and clustering algorithms in order to achieve the purpose of this study.

The resulted models evidenced that the space heating energy consumption not only depends on the characteristics of the building itself but also on the urban characteristics. It was found that, besides the heated volume, the floor area and the surface-to-volume ratio, other important variables influence the energy consumptions. The floor area was positively correlated with the energy consumption while the surface-to-volume ratio showed a curved relationship. At urban scale, the most influential variables were the heated degree days and the albedo; this last was negatively correlated with the space heating consumption. The most important socio-economic variables were the percentage of working people with a positive correlation and the percentage of young inhabitants with a negative correlation.

Furthermore, the cluster analysis evidenced the possibility of grouping buildings with similar energy consumptions and characteristics. This analysis can be useful for policy makers in defining retrofit interventions for every specific group of buildings to improve the energy performance of a city or to identify new more effective energy performance benchmarks.

In conclusion, the statistical GIS-based methodology proposed in this study is quite simple and also replicable to other urban contexts. With this methodology, urban planners, architects and decisions makers can identify an effective solution based on the real buildings heritage to reduce energy consumptions and the relative GHG emissions at neighborhood or urban scale.

Short Biography


Talk Keywords
Target Audience
Engineering students, post-doctoral researchers, industry and national laboratories researchers, professors
Speaker-intro video

The International Conference on Innovative Applied Energy (IAPE’18)