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Renovation at district scale is a key strategy to reduce CO 2 emissions by optimising the implementation of renewable energy sources and taking advantage of economy of scale. This paper focuses on analysing good practice examples on energy renovations at district scale. The paper adapts a qualitative research methodology in four phases, including the multi-perspective analysis of nine exemplary renovation projects in six European countries, including identification of drivers and barriers of different stakeholders.It is found that the drivers for a district renovation are not restricted to energy savings, but typically also include improving the overall quality of life as well as the image and economic value of a district. Moreover, the need for financial models that can alleviate split-incentive problems between investors and resident organizations is identified.Barriers for carrying out a district renovation include that there is a need to comply with energy standards, that the renovation scope had to be limited to avoid a noticeable rent increase and that resettling of tenants during the renovation is often not possible.Lessons learned include that good communication amongst the different stakeholders, especially with residents, plays a key role for the success of the project. Furthermore, a strong leadership is needed to coordinate activities due to the great number of stakeholders.

Assessing the existing building stock’s hourly energy demand and predicting its variation due to energy efficiency measures are fundamental for planning strategies towards renewable-based Smart Energy Systems. However, the need for accurate methods for this purpose in the literature arises. The present article describes a GIS-based procedure developed for estimating the energy demand profiles of urban buildings based on the definition of the volumetric consistency of a building stock, characterized by different ages of construction and the most widespread uses, as well as dynamic simulations of a set of Building Energy Models adopting different energy-related features. The simulation models are based on a simple Building Energy Concept where selected thermal zones, representative of different boundary conditions options, are accounted. By associating the simulated hourly energy density profiles to the geo-referenced building stock and to the surveyed thermal system types, the whole hourly energy profile is estimated for the considered area. The method was tested on the building stock of Milan (Italy) and validated with the data available from the annual energy balance of the city. This procedure could support energy planners in defining urban energy demand profiles for energy policy scenarios.