Suchergebnisse
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.
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.
GeoDatKlim - Geo Data and Satellite Data for Carbon Neutral Cities
The Vienna Geospace Hub innovation lab will help optimize the application of geospatial and satellite data to solve complex urban challenges. The innovation lab serves as a networking platform for administration, science, economy, as well as a development and test environment for innovative use cases.
Topview - Methodology for the efficient use of remote sensing data for climate change adaptation and spatial energy planning
Development of integrated approaches to sustainable energy and heat planning in urban areas by utilising remote sensing data and geo-information-based technologies for decision-making in the planning of energy infrastructures and climate adaptation measures.
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.
MaBo - material saving in bored piles - a contribution to reducing CO2-emissions in the construction industry
Development of an innovative method for saving material in bored piles in order to reduce CO2 emissions in the construction industry. By optimizing the construction methods and using alternative materials, the sustainability of the foundation bodies is to be improved.
QualitySysVillab - Protecting sustainable qualities in neighbourhood developments through process control and new digital methods
Development of a process concept to bring sustainable qualities in neighbourhood development from the intention and announcement level to the built reality. The process is supported by digital methods of energy and structural design and evaluated in the context of a case study.
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.
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.
GreenFDT – Green Façade Digital Twin
In an interdisciplinary framework, the possibilities for optimizing the rear ventilation distance of façade greening elements and their potential impact on indoor and urban climate are being investigated. The precise and comprehensive investigation of these relationships is made possible by the extensive deployment of sensors and measuring tools and furthermore the development and integration of a digital twin in a BIM model.
ThermEcoFlow: Innovative Technologien & Methoden für Raumluftkomfort und Energieoptimierung in Thermengebäuden
ThermEcoFlow setzt sich zum Ziel den Energieverbrauch von Thermen durch verbesserte Simulationsmodelle und KI-gestützte Regelungen zu optimieren. Durch präzisere Modellierung von Luftströmungen, Feuchtigkeitslasten und Verdunstungen und KI-gestützten Steuerungssystemen soll der Energieverbrauch und die CO2-Emissionen langfristig gesenkt und der Raumkomfort für Besucher:innen verbessert werden.
BATTMON - Erhöhung der nutzbaren Ladekapazität, Lebensdauer und Sicherheit von Batteriespeichern im urbanen Raum
Ziel von BATTMON ist die Entwicklung einer verbesserten Zustandsbestimmung von Batteriespeichern für Anwendungen in Gebäuden und Quartieren. Dazu werden flächenhafte Foliensensoren zur ortsaufgelösten Messung von Temperatur und Druck entwickelt. Mit diesen Daten sollen der Ladezustand aber auch der Gesundheitszustand genauer abgeschätzt und Zellschäden frühzeitig erkannt werden, um die Brand- und Explosionsgefahr zu reduzieren.
SELF²B - Selbstdiagnostizierende Gebäude, HLK- und PV-Systeme für die nächste Generation energieeffizienter Betriebsführung
SELF²B entwickelt und demonstriert eine KI-basierte, selbstlernende und selbstdiagnostizierende Fehlererkennungs- und Diagnoselösung für HLK- und PV-Anlagen in Pilotgebäuden, ausgestattet mit Gebäudeautomationssystemen, in Wien. Die Innovation geht über den aktuellen Stand der Technik hinaus, indem sie semantische Daten, Ontologien und maschinelles Lernen kombiniert. Ziel ist es, Energieeinsparungen und Effizienzsteigerungen im Gebäudebetrieb zu erzielen und die Technologie auf breiter Basis nutzbar zu machen.
SPOT – Smartes Stellplatz-Optimierungstool
SPOT entwickelt ein datengetriebenes Tool zur bedarfsgerechten Optimierung von Stellplätzen in urbanen Räumen, um Flächen effizienter zu nutzen und die Klimaneutralität zu fördern. Das Tool unterstützt Städte bei der Reduktion von Parkplatzflächen und der Schaffung von Grünflächen, indem es evidenzbasierte Stellplatzschlüssel berechnet.
QualitySysVillab - Sicherung nachhaltiger Qualitäten in Quartiersentwicklungen durch Prozesssteuerung und neue digitale Methoden
Entwicklung eines Prozess-Konzeptes um nachhaltige Qualitäten in der Quartiersentwicklung von der Absichts- und Ankündigungsebene in die gebaute Realität zu bringen. Der Prozessablauf wird durch digitale Methoden der Energie- und Tragwerksplanung unterstützt und im Rahmen einer Case-Study evaluiert.