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In 2020, domestic buildings represented 27% of the energy consumption in the European Union. The EC Renovation Wave concluded that the annual renovation rate should be doubled, from 1% to 2%, over the next decade in order to meet the 2030 energy efficiency targets. Funding programs are being implemented at national and European levels to accelerate this process. These aim to prioritize large-scale interventions and the most efficient energy conservation measures by including strict performance targets as eligibility conditions. However, these programs are coordinated by local authorities, and they struggle to generate reliable energy estimates for large areas, not least predict potential savings at both aggregated and individual levels. This paper presents a bespoke energy retrofit analysis methodology based on urban building energy modelling, which is defined and calibrated with the Energy Performance Certificate Database. We apply this method to the case study Barrio das Flores. The model adopts a hybrid approach, defining and simulating representative typologies to extrapolate the results to the whole district according to each dwelling’s specific parameters. This methodology will be used to predict the potential energy savings in the study area and support the grant application for the funding renovation program.

New modelling tools are required to accelerate the decarbonisation of the building sector. Urban building energy modelling (UBEM) has recently emerged as an attractive paradigm for analysing building energy performance at district and urban scales. The balance between the fidelity and accuracy of created UBEMs is known to be the cornerstone of the model’s applicability. This study aimed to analyse the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance, namely, (1) the level of detail (LoD) of the buildings’ geometry; (2) thermal zoning; and (3) the surrounding shadowing environment. The analysis was conducted for two urban areas in Stockholm (Sweden) using MUBES—the newly developed UBEM. It is a bottom-up physics-based open-source tool based on Python and EnergyPlus, allowing for calibration and co-simulation. At the building scale, significant impact was detected for all three factors. At the district scale, smaller effects (<2%) were observed for the level of detail and thermal zoning. However, up to 10% difference may be due to the surrounding shadowing environment, so it is recommended that this is considered when using UBEMs even for district scale analyses. Hence, assumptions embedded in UBEMs and the scale of analysis make a difference.

Residential buildings are an important sector in the urban environment as they provide essential dwelling space, but they are also responsible for a significant share of final energy consumption. In addition, residential buildings that were built with outdated standards usually face difficulty meeting current energy performance standards. The situation is especially common in Europe, as 35% of buildings were built over fifty years ago. Building retrofitting techniques provide a choice to improve building energy efficiency while maintaining the usable main structures, as opposed to demolition. The retrofit assessment requires the building stock information, including energy demand and material compositions. Therefore, understanding the building stock at scale becomes a critical demand. A significant piece of information is the building geometry, which is essential in building energy modelling and stock analysis. In this investigation, an approach has been developed to automatically measure building dimensions from remote sensing data. The approach is built on a combination of unsupervised machine learning algorithms, including K-means++, DBSCAN and RANSAC. This work is also the first attempt at using a vehicle-mounted data-capturing system to collect data as the input to characterise building geometry. The developed approach is tested on an automatically built and labelled point cloud model dataset of residential buildings and shows capability in acquiring comprehensive geometry information while keeping a high level of accuracy when processing an intact model.

Greenhouse gas emissions depend on natural and anthropic phenomena; however, to reduce emissions, we can only intervene in terms of anthropic causes. Human activity is very different in various countries and cities. This is mainly due to differences in the type of urban environment, climatic conditions, socioeconomic context, government stability, and other aspects. Urban building energy modeling (UBEM), with a GIS-based approach, allows the evaluation of all the specific characteristics of buildings, population, and urban context that can describe energy use and its spatial distribution within a city. In this paper, a UBEM is developed using the characteristics and consumption of eight typical buildings (archetypes) in the climate zone of Santiago de Chile. The archetype-based UBEM is then applied to the commune of Renca, a critical suburb of Santiago, with the use of QGIS to analyze the energy demand for space heating and the potential for energy saving after four retrofitting interventions. Knowing the costs of the retrofitting interventions and the energy price, the simple payback time was evaluated with the reduction in GHG emissions. Starting from the actual building stock, the results show that the most effective retrofitting intervention for the commune of Renca is the thermal insulation of walls and roofs; due to the type of dwellings, this particular intervention could be more convenient if associated with the installation of solar technologies. This methodology can be replicated with the data used by urban planners and public administrations available for many Chilean cities and in other countries.

Social houses built after the Second World War to accommodate workers and low-income families represent one of the major energy consumers and greenhouse gas emitters in the residential sector. Plans for their renovation are underway in all European countries, and the process is more complicated for Italian cities due to the lack of space and the large number of historical buildings. This study addresses this challenge by proposing a methodology to renovate a low-income district in the city of Venice using CityBES to model and evaluate energy conservation measures. CityBES is a web-based tool that allows users to employ urban building energy modeling for large-scale energy and retrofit analyses of building stocks. In the case study conducted for Venice's Santa Marta district, due to the particular context, four common energy conservation measures covering both the building envelope and heat generation boilers have been applied. The evaluation of energy-saving performances at the district level showed that the four measures together achieved 67% energy savings, an abatement in energy cost equal to 67%, and annual carbon dioxide emissions reduction of 1.1 MtCO2. The case study demonstrates a method and workflow replicable for energy retrofit analysis of building stocks in other historical districts.

Buildings represent 40% of the European Union’s final energy consumption and are largely of resi- dential use. From 2006 to 2016, existing European housing stocks have been analysed at national level to make the energy refurbishment processes transparent and effective. However, at the meta- scale of regions, cities or neighbourhoods, case-by-case analysis using Building Energy Models (BEM) becomes an unfeasible decision-support tool. To try to overcome this limitation, the nascent field of Urban Building Energy Modelling (UBEM) is making substantial progress in the assessment of build- ing energy performance at urban scale. Still, most of the UBEM projects rely upon archetypes – i.e. virtual or sample buildings illustrative of the most frequent characteristics of a particular category, and the definition and description of such archetypes may compromise their reliability. This paper presents an alternative UBEM approach, especially designed for the homogeneous historic districts of cities where a significant proportion of the buildings are under preservation rules. These rules can restrict the scope of the measures to improve their energy efficiency or limit the possibility of implementing renewable energy systems. We introduce a new parameter (HAD) to classify blocks according to their heritage asset density. HAD is then mapped onto the study-area and the sample block is selected as representative of the most frequent HAD category. Using the historic ensemble of Seville as case-study, this paper shows results in energy consumption on a district scale and proposes a set of solutions to improve the energy efficiency of the buildings while respecting the heritage preservation rules. To sup- port consistent policy decisions, validation of these results has been carried out, by in-situ monitoring of a representative number of dwellings.