Research Projects
There are 41 results.
ThermEcoFlow: Innovative technologies and methods for indoor air comfort and energy optimisation in thermal spa buildings
ThermEcoFlow aims to optimize the energy consumption of thermal spas facilities through improved simulation models and AI-supported control systems. By precisely modelling airflow, humidity loads, and evaporation, combined with AI-driven regulation, the project seeks to reduce energy consumption and CO₂ emissions in the long term while enhancing indoor comfort for visitors.
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.
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.
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.
Circular Bio Floor- Floor construction made from biomaterials
In this project biogenic building materials from wood industry waste and geopolymer binders are developed that can be used as tamped fill or 3D-printed dry-screed elements in timber construction. These materials offer functional benefits and an excellent eco-balance, contribute to the conservation of forests and enable the production of separable and reusable floor segment panels using digital manufacturing technologies. That significantly reduces the consumption of primary raw materials.
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.
IMPACT – Hybrid hydraulic and electric charging of stratified compact hot water
The IMPACT project is developing an innovative decentralised hot water storage technology for large-volume urban housing. Thanks to a novel, flat design, the system enables highly efficient utilisation of renewable energy sources such as heat pumps and photovoltaics. The aim is to create a cost-efficient, sustainable solution for decarbonising water heating that is optimised using intelligent energy management and machine learning methods.
FlexHP - AI-supported control models for optimising the flexibility of heat pumps to reduce the load on the electricity grid
Development of a new type of energy management system for heat pumps that enables methods for intelligent heat pump operation and thus maximises flexibility. This requires forecast-based models for control that utilise technologies such as machine learning.
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.
BIM.sustAIn - Artificial Intelligence to enhance sustainability in BIM projects
The construction sector faces growing challenges in meeting sustainability requirements, particularly during early project phases where key decisions on materials, construction methods, and energy concepts are made. This project aims to leverage AI and BIM to optimize sustainability assessments by providing precise CO₂ balance forecasts and material suggestions. The innovative approach reduces manual effort and supports the implementation of climate-neutral construction, contributing significantly to Austria’s climate goals.
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.
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.
SAGE - scalable multi-agent architectures for facility management and energy efficiency
The SAGE project is developing scalable multi-agent architectures that enable buildings to recognize operational anomalies autonomously and react dynamically to environmental changes. The integration of multi-agent architectures in combination with Large Language Models (LLMs) and the development of a human-in-the-loop approach will optimize the collaboration between humans and machines. These solutions should significantly reduce the energy consumption of buildings and increase user-friendliness.
RIGOR - Towards reproducible, transparent, and valid AI methods for buildings and cities
The RIGOR project investigates the actual added value and scientific reliability of AI-based methods in the context of buildings, districts, and cities. It focuses on reproducibility, transparency, and the objective comparison of modern AI approaches with simple, robust baseline models. The goal is to establish an evidence-based foundation for the responsible use of artificial intelligence in energy- and safety-critical applications in the built environment.
GreenGEO - Data-based integration of climate change adaptation measures into spatial planning
Green and blue infrastructure (GBI) is a key instrument in the fight against climate change. Nevertheless, deciding where and in what form it should be used most effectively remains a challenge in spatial planning practice. The development of a digital model that links location-specific climate risk data with suitable GBI measure proposals will make this much easier and more objective.
FacilityQ - Machine-readable building data for building operation
FacilityQ develops an AI-supported, standards-based workflow for the automated and seamless transfer of planning and construction data to building operations. Semantic data modeling, document classification, and interoperable interfaces create a scalable process that drastically reduces the effort and costs when starting building operation.
NeoEAI4Control - Neuro-Symbolic Edge AI for Efficient and Robust Control in Energy Management
Increasing energy efficiency in buildings is a key goal of the energy transition, which, together with increasing digitalization, is leading to new requirements for intelligent control and regulation. Traditional control methods are reaching their limits. As part of the project, we propose the use of edge AI with specialized neuromorphic chips to enable scalable, decentralized, efficient, and real-time control in buildings.
StatiPLANT - Exploration of a standardized model for the static assessment of climbing plants
The StatiPLANT project aims to systematically record and analyze static loads on vertically greened facades caused by climbing plants under the influence of wind, tensile forces, and dead loads of the plants. The goal is to develop a conceptual, parameterizable assessment model as the basis for a standardized design procedure in structural engineering.