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Building operation is responsible for 28% of the world’s carbon emissions. In this context, establishing priorities in refurbishment strategies at the scale of a city or a group of buildings is important. Such procedures are usually led by experts in energy performance and, therefore, they are rarely carried out due to their long and costly nature. This research aims at the estimation of building energy performance to pave the way towards finding near-optimal refurbishment strategies. Thanks to the identification of easily-accessible building characteristics, the method applies machine learning models to scan a building portfolio based on a low level of details. The results show good potential to identify low-performer buildings with simple machine learning methods. It also opens the door for further improvements through the inclusion of supplementary building features at the input of the predictive system. This work includes (a) the integration of a knowledge database thanks to the Swiss CECB energy performance certificates, referencing more than 70 000 buildings, (b) the preparation of a training data set through the selection of relevant physical characteristics of buildings (input) and the corresponding energy consumption labels (output), (c) the development of predictive models used in a supervised way, (d) their evaluation on an independent test set.

The building stock is responsible for 24% of carbon emissions in Switzerland and 44% of the final energy use. Considering that most of the existing stock will still be in place in 2050, it becomes essential to better understand this source of emissions. Although the Swiss Cantonal Energy Certificate for Buildings (CECB) database has been used in previous research, no comprehensive characterization of the buildings can be found. This data paper presents an analysis and classification of the Swiss building stock based on the data found in the database. The objective is to create a knowledge foundation that can be used in future research on the performance of existing buildings. Using a sample of almost 50,000 buildings and a Python script, datasheets were created for single-family houses and multi-family houses for nine construction periods. These archetypes are described through selected available energy-related parameters, such as energy reference area, U-values, and energy source with indicators such as median, 25th percentile, and 75th percentile or distributions. The resulting data can be used for different purposes: (1) to calibrate energy models; (2) for analysis that requires scaling-up strategies to the whole stock; and (3) to identify weak and/or relevant classes of buildings throughout the stock.

Target values for creating carbon budgets for buildings are important for developing climate-neutral building stocks. A lack of clarity currently exists for defining carbon budgets for buildings and what constitutes a unit of assessment—particularly the distinction between production- and consumption-based accounting. These different perspectives on the system and the function that is assessed hinder a clear and commonly agreed definition of ‘carbon budgets’ for building construction and operation. This paper explores the processes for establishing a carbon budget for residential and non-residential buildings. A detailed review of current approaches to budget allocation is presented. The temporal and spatial scales of evaluation are considered as well as the distribution rules for sharing the budget between parties or activities. This analysis highlights the crucial need to define the temporal scale, the roles of buildings as physical artefacts and their economic activities. A framework is proposed to accommodate these different perspectives and spatio-temporal scales towards harmonised and comparable cross-sectoral budget definitions. 'Policy relevance' The potential to develop, implement and monitor greenhouse gas-related policies and strategies for buildings will depend on the provision of clear targets. Based on global limits, a carbon budget can establish system boundaries and scalable targets. An operational framework is presented that clarifies greenhouse gas targets for buildings in the different parts of the world that is adaptable to the context and circumstances of a particular place. A carbon budget can enable national regulators to set feasible and legally binding requirements. This will assist the many different stakeholders responsible for decisions on buildings to coordinate and incorporate their specific responsibility at one specific level or scale of activity to ensure overall compliance. Therefore, determining a task specific carbon budget requires an appropriate management of the global carbon budget to ensure that specific budgets overlap, but that the sum of them is equal to the available global budget without double-counting.

The data presented in this article are related to the research article entitled “Temporal variations in the primary energy use and greenhouse gas emissions of the electricity provided to the Swiss grid” Vuarnoz and Jusselme (2018). The provided data are the hourly CO2-eq emission factors, and the hourly conversion factors for the cumulative energy demand and its non-renewable part for the Swiss electricity mix over one year. These data have been assessed on the basis of an inventory of the technology used for electricity generation and an attributional life-cycle approach. The presented data are necessary for life-cycle assessment of all processes and products using electricity in Switzerland. They serve also as a sustainable benchmark when implementing renewable energy systems and energy storage.

Free cooling strategies are gaining importance in design practice due to the increased risk of overheating in well-insulated buildings with high internal loads such as offices. The state of the art highlights that the most efficient passive solution for indoor temperature stabilization and control is the integration of thermal mass with an optimized ventilative cooling profile to enhance the thermal cycle of heat storage. Due to its cyclical behavior, thermal mass effects are difficult to predict and quantify with the traditional steady-state approach to building thermal performance. Dynamic thermal simulations help to assess a building’s behavior under transient situations, including the thermal mass influence. However, building codes usually include thermal simulations based on standard assumptions: typical meteorological year (TMY), standard occupancy, standard daily-based lighting and appliances profiles, and standard weekly-based occupancy. Thus, when assumptions change, the actual behavior of the building may vary consistently from the predicted conditions. In this paper, we focused on the ability of thermal mass to contrast the influence of variations from the standard assumptions, especially in relation to climate and ventilation profiles. The results show the necessity of encompassing different risk scenarios when evaluating a free cooling solution performance. Among the different scenarios simulated, natural ventilation misuse shows greater influence on the thermal indoor environment, especially if coupled with low thermal mass.