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This paper introduces three novel approaches to size geothermal energy piles in a MILP, offering fresh perspectives and potential solutions. The research overlooks MILP models that incorporate the sizing of a geothermal borefield. Therefore, this paper presents a new model utilizing a g-function model to regulate the power limits. Geothermal energy is an essential renewable source, particularly for heating and cooling. Complex energy systems, with their diverse sources of heating and cooling and intricate interactions, are crucial for a climate-neutral energy system. This work significantly contributes to the integration of geothermal energy as a vital energy source into the modelling of such complex systems. Borehole heat exchangers help generate heat in low-temperature energy systems. However, optimizing these exchangers using mixed-integer-linear programming (MILP), which only allows for linear equations, is complex. The current research only uses R-C, reservoir, or g-function models for pre-sized borefields. As a result, borehole heat exchangers are often represented by linear factors such as 50 W/m for extraction or injection limits. A breakthrough in the accuracy of borehole heat exchanger sizing has been achieved with the development of a new model, which has been rigorously compared to two simpler models. The geothermal system was configured for three energy systems with varying ground and bore field parameters. The results were then compared with existing geothermal system tools. The new model provides more accurate depth sizing with an error of less than 5 % compared to simpler models with an error higher than 50 %, although it requires more calculation time. The new model can lead to more accurate borefield sizing in MILP applications to optimize energy systems. This new model is especially beneficial for large-scale projects that are highly dependent on borefield size. [ABSTRACT FROM AUTHOR]

Building energy system management is critical for resource-saving approaches amid climate change-driven energy transitions. This paper presents a digital twin toolchain leveraging modern technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR). The toolchain automates the derivation of georeferenced digital twins during Technical Building Equipment (TBE) commissioning. Using a Scan vs. BIM process, discrepancies between as-planned and as-built TBE are identified, allowing automatic updates to the BIM model. Validation methods ensure both physical and functional aspects of the TBE are accurate. VR and AR facilitate off- and on-site commissioning, enabling immersive visualization and live sensor data access. An evaluation in small and large-scale demonstrators shows the toolchain's scalability and efficiency, with promising results in performance and accuracy. Future work aims to integrate more operational data, enhancing the digital twin's capabilities for building energy system management. [ABSTRACT FROM AUTHOR]

Future weather scenarios significantly affect indoor thermal comfort, influencing people's well-being and productivity at work. Thus, future weather scenarios should be considered in the design phase to improve a building's climate change resilience for new constructions as well as renovations in building stock. As thermal comfort is highly influenced by internal and external thermal loads resulting from weather conditions and building usage, only a dynamic building performance simulation (BPS) can predict the boundary conditions for a thermal comfort analysis during the design stage. As the model setup for a BPS requires detailed information about building geometry, materials, and usage, recent research activities have tried to derive the required simulation models from the open BIM (Building Information Modeling) Standard IFC (Industry Foundation Classes). However, even if IFC data are available, they are often faulty or incomplete. We propose a template-based enrichment of the BPS models that assists with imputing missing data based on archetypal usage of thermal zones. These templates are available for standardized enrichment of BPS models but do not include the required parameters for thermal comfort analysis. This study presents an approach for IFC-based thermal comfort analysis and a set of zone-usage-based templates to enrich thermal comfort input parameters. [ABSTRACT FROM AUTHOR]

Climate neutrality goals in the building sector require a large-scale estimation of environmental impacts for various stakeholders. Life Cycle Assessment (LCA) is a viable method for this purpose. However, its high granularity, and subsequent data requirements and effort, hinder its propagation, and potential employment of Machine Learning (ML) applications on a larger scale. The presented paper outlines the current state of research and practice on district-scale building LCA in terms of standards, software and certifications, and data availability. For this matter, the authors present the development and application of two district-scale LCA tools, Teco and DisteLCA, to determine the Global Warming Potential (GWP) of three different residential districts. Both tools employ data based on (including, but not limited to) CityGML, TABULA, and ÖKOBAUDAT. The results indicate that DisteLCA's granular approach leads to an overestimation of environmental impacts, which can be derived from the statistical approach to operational energy use and related emissions. While both tools lead to substantial time savings, Teco requires less manual effort. The linkage of the aforementioned data sources has proven laborious and could be alleviated with a common data framework. Furthermore, large-scale data analysis could substantially increase the viability of the presented approach. [ABSTRACT FROM AUTHOR]

Heating and ventilation in buildings and vehicles are responsible for over a third of global final energy usage and the resulting emissions. Local climatization can help to save energy while at the same time enable more individualized and adapted micro-climates around people. For the domain of local comfort, integrative test environments for sensors, actuators, and control software are scarce, and oftentimes tailored to a specific set of components. Here, a server-based modular testing infrastructure which allows integration and evaluation of sensors, actuators, and control strategies is presented. Currently, the system is able to integrate, monitor, and log data of thermal imaging, motion sensing, environmental sensor such as temperature or air velocity and to forward signals to actuators such as fans, infrared- or contact-heaters. The generation of control signals is model-based and relies on user feedback provided via the system's smartphone app. Lastly, learning algorithms can be trained and compared during user studies. Practical Application: Although developed in a laboratory based research context, the proposed system is based on open standards and protocols. It therefore can be applied by practitioners, developers, and manufacturers in order to test stand-alone components as well as ensembles of sensors and actuators for personalized climatization in an integrative and replicable manner. [ABSTRACT FROM AUTHOR]