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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. [ABSTRACT FROM AUTHOR]

Residential buildings have a substantial impact on global environmental impacts. In this paper we present a model that assesses impacts from materials (embodied impacts) as well as from heating (operational impacts) for all residential buildings in Switzerland. Our model builds on an existing high-resolution spatio-temporal building stock model, which estimates the space heating demand of buildings, based on building property and site data. In this work we additionally include the impacts of materials extraction, production, transportation, and disposal of building components (floors, roofs, walls, ceilings and windows). Our model comes with a site- and building-specific default parametrization for all buildings, but it is developed as a tool that allows architects and planners to adapt building and site data with specific information to increase the accuracy of the environmental assessment of the current buildings and future building-management scenarios. We applied the model to ten Swiss buildings and assessed a selection of building-management scenarios to illustrate the application of the tool. The results show that our model estimates fairly accurate values of thermal and ventilation heat transmittance, operational space heating demand, and operational and embodied climate-change impacts for the considered buildings. The detailed modeling results for the ten case study buildings outperformed the simplified version of the model with generic default values for material building composition (using age-grouped building archetypes), emphasizing the importance of adding detailed material information in building life cycle assessments. Therefore, a focus should be placed on obtaining accurate material inventories of buildings in future research. The here-developed model allows for integration of such detailed material inventories, overwriting the provided default values. • Tool provides assessment of embodied and operational impacts of the entire Swiss residential building stock. • Tool estimates close to accurate space heating demands of buildings. • Tool is equipped with generic default building and site data. • Tool allows overwriting generic values with data inputs from building stakeholders, which is demonstrated in the case study of 10 buildings. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized due to the fact that energy demands are often assumed to be fixed quantities rather than controlled dynamic variables. The joint optimization of energy hubs and buildings' energy management systems can result in higher energy savings. This paper investigates how different MPC strategies perform on energy management systems in buildings and energy hubs. We first discuss two MPC approaches; centralized and decentralized. While the centralized control strategy offers optimal performance, its implementation is computationally prohibitive and raises privacy concerns. On the other hand, the decentralized control approach, which offers ease of implementation, displays significantly lower performance. We propose a third strategy, distributed control based on dual decomposition, which has the advantages of both approaches. Numerical case studies and comparisons demonstrate that the performance of distributed control is close to the performance of the centralized case, while maintaining a significantly lower computational burden, especially in large-scale scenarios with many agents. Finally, we validate and verify the reliability of the proposed method through an experiment on a full-scale energy hub system in the NEST demonstrator in Dübendorf, Switzerland. [ABSTRACT FROM AUTHOR]