Recent studies demonstrated how the energy consumption of buildings is composed by two main components: one related to the building's envelope, systems efficiencies, and climate; the other related to the surrounding environment, the urban context and the locally variations of microclimate.

At building scale, the energy consumption is estimated at the design stage, through energy simulation tools describing the buildings in details, the climate, and evaluating different solutions of the project or of the materials used, in order to achieve higher energy efficiency. The energy consumption is also influenced by the urban context; the interaction between the building and urban context, considering also the local variation of the microclimate, complicates the decision making processes and it will have an important role especially for nearly zero energy buildings with a minimum contribute in energy consumption of the building itself. However, considering the urban context will allows a more accurate design benefitting both in terms of energy and costs.

The aim of this study is to understand how the urban form of the city can influence the buildings' energy consumptions for space heating and, therefore, to analyze the energy sustainability at urban scale, starting from the knowledge of energy consumptions at building scale. Through a literature review, factors of urban morphology which mostly affect energy consumptions of buildings have been identified, and subsequently the relationship between some of them to define the variation of energy consumption has been evaluated.

Particularly, the energy-use models for space heating consumption of buildings are investigated considering a hybrid approach with bottom-up and top-down models. Then, the energy-use variations were also correlated with the characteristics of the different built areas as the urban morphology, the solar exposure of urban spaces and the albedo coefficients of outdoor surfaces. This study describes an iterative methodology based on a GIS tools to manage and to represent buildings' information about energy consumptions of big datasets at urban scale.