Suchergebnisse
Climbing plant NAVI - Webapplication for the reliable selection of climbing plants
The comprehensive web application "Climbing Plants NAVI" takes important parameters such as the type of greening, weight classes, toxicity, ecological value, the possibility of culinary use, into account and thereby supports the selection of suitable climbing plants for greening projects. The climbing plants NAVI is not only intended for municipal representatives, planners and other professionals, but is also directed at private individuals.
iLESS - Intelligent load profile analysis to maximize self-consumption of solar power
The goal is to reconstruct the individual contributions of various devices from existing load profile curves. This problem is of fundamental importance in the context of maximizing self-consumption of solar power by private households.
cityclimAIte - Study on AI applications to achieve and support climate-neutral cities
The central task of this study is to provide decision-makers with a comprehensive overview and to derive recommendations for the efficient use and benefits of AI based on the estimated effects. The aim is to strengthen national value chain in this field of technology through appropriate, supportive framework conditions.
TEA-PUMP – Techno-economic Analysis of Thermoelectric Modules for Efficiency and Performance Enhancement in Heat Pumps for Residential Buildings
The TEA-Pump project explores the innovative use of thermoelectric elements (TEM) in compression heat pumps to enhance their efficiency and performance. Through a comprehensive techno-economic analysis, promising heat pump (HP) configurations for use in urban multi-family housing are identified. The project makes a significant contribution to the decarbonization of heating and cooling supply and supports the development of climate-neutral cities through energy-efficient, future-oriented heat pump technologies.
DataScience4SmartQuarters - Energy saving potentials through neighbourhood and community planning – Part 2
DataScience4SmartQuarters develops and researches an innovative method for the fast and efficient evaluation of simulation scenarios (building/energy, mobility) for communities.
TOPS – Topology-optimised reinforced concrete slabs with digital formwork and reinforcement
The TOPS project is investigating material-efficient ribbed concrete slabs, which save up to 50% of the concrete used in conventional flat slabs by topology-optimisation. A 'file-to-factory' process enables the automated production of formwork and reinforcement using digital technologies. The construction method reduces CO₂ emissions and contributes to the decarbonisation of the construction industry.
SPOT – Smart Parking Space Optimization Tool
SPOT develops a data-driven tool for demand-oriented optimization of parking spaces in urban areas to use space more efficiently and promote climate neutrality. The tool supports cities in reducing parking areas and creating green spaces by calculating evidence-based parking space ratios.
BOSS - Building Energy Systems on causal reasoning
The project develops novel Causal AI methods for automated fault detection in buildings. It aims to derive semantic structures from time series data and transparently model cause-effect relationships. This provides the foundation for scalable, explainable FDD solutions to reduce energy consumption and emissions in the building sector.
SIMPLE AD Evaluator - S.I.M.P.L.E. Sustainable Integration Modeling and Predictive Leveraging Evaluator
The SIMPLE AD Evaluator fills an existing gap in sustainable local planning by providing a low-threshold and collaborative evaluation tool for early planning phases. By linking questionnaires with System Dynamics models, the tool delivers well-founded decision-making foundations and customized sustainability checklists. This supports municipalities, project developers, and decision-makers in achieving a strategic and cost-efficient sustainable transformation from concept to implementation.
Kimoni – Artificial Intelligence for Monitoring the Performance of Green Infrastructure
Kimoni develops an AI-based tool for high-resolution analysis and assessment of Green Infrastructure for climate change adaptation. By combining satellite and geospatial data with machine learning, Kimoni provides a cost-efficient and scalable solution to comply with the EU Taxonomy and optimize climate-friendly investments.
Twin2Share - Digital twins for energy optimization in energy communities (ECs)
Digital twins to support energy communities over their life cycle. The project focuses on optimizing energy efficiency and costs, dynamic load management and the integration of users to promote sustainable energy use and the stabilization of the electricity grid.
ReSpace – Reclaiming Spaces
ReSpace is developing an AI-based model for identifying, categorizing, and activating sealed areas. Existing data sources (aerial and satellite images, mobile network data, land registry entries) are integrated and enhanced with dynamic analysis to derive evidence-based recommendations for action.
AI4FM - Artificial Intelligence for Facility Management
AI-based anomaly and fault detection in buildings. Digital twins of buildings with simulation models for testing and optimizing rule-based fault detection methods. Mining of the recorded time-series data from existing Building Management Systems to train Machine Learning models for fault detection.
ReCon: Development of a resilient hook-and-loop-fastening-system for the adaptable assembly of building components in the building industry
Systemic examination of the hook-and-loop fastener and building component interfaces for the development of a resilient fastening system between parts/components with different functions and lifespan. The result serves to verify the fastening system and forms a basis for further research and establishment in the building industry.
TWIN - Digital twins for sustainable buildings
Digital building twins have hardly been used in practice due to an often unfavourable cost-benefit ratio. The aim of the TWIN project is to bring together use cases of digital building twins with a high ecological and economic impact in order to prepare application scenarios with great implementation potential.
OctoAI: The next generation of high-performance edge AI for smart buildings
Current IoT (Internet of Things) solutions for buildings depend almost exclusively on cloud infrastructure and cloud-based services. In the OctoAI project, we are developing the next generation of high-performance Edge AI (Artificial Intelligence) for smart buildings. In OctoAI, we combine the concept of edge AI with user-centric energy services and test two edge-ready applications.
Beyond - Virtual Reality enabled energy services for smart energy systems
The Beyond project addressed two different energy services through the interaction of various technologies: state-of-the-art virtual reality (VR), the Internet of Things (IoT), energy simulation and machine learning (ML). Energy service 1 immerses VR users in energy-efficient and sustainable building design through an educational VR game, while energy service 2 improves the maintenance of building systems, particularly heating, ventilation and air conditioning (HVAC) systems, through the application of ML models for predictive maintenance.
KityVR - Artificial intelligence techniques to implement CityGML models and VR visualization
The KityVR project deals with key research aspects at the interface between 3D city models and virtual reality. It addresses questions such as how virtual reality can be used for visualization in the field of 3D city models and how machine learning and statistical methods can be used to enrich or complete energy-related data sets.
BIMpeco - Environmentally relevant product data in collaborative BIM environments
Construction products can pose a risk to the environment and health due to their pollutant content or releases. In the BIMpeco project, workflows and data structures for digital information management of this environmentally relevant product data are developed. For this purpose, the new ISO standards ISO 23387 and ISO 19650-1 are tested and synchronized with established process flows. The project results will be made available on an open-source basis and can be integrated into any Common Data Environment (CDE) that complies with the standards mentioned. The BIMpeco project is the first to lay the foundations for product information management of environmentally relevant properties in the CDE, covering the entire lifecycle and supply chain.
sustAIn4Build - AI competence for sustainable building management in climate neutral cities
The objective of the project sustAIn4Build is to increase energy efficiency and sustainability in the building sector by using artificial intelligence (AI). Industry-specific training programs support Austrian companies to develop a workflow for integrating AI technologies into their processes, enabling them to develop resource-saving, cost-effective and sustainable solutions. This strengthens their competitiveness and contributes to the achievement of European decarbonisation goals.