Urban sustainability

Innovative simulation tools

Real-life models

Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability

MOEEBIUS introduces a Holistic Energy Performance Optimization Framework that enhances current modelling approaches and delivers innovative simulation tools which deeply grasp and describe real-life building operation complexities in accurate simulation predictions that significantly reduce the “performance gap” and enhance multi-fold, continuous optimization of building energy performance as a means to further mitigate and reduce the identified “performance gap” in real-time or through retrofitting.

 

The revised version of MOEEBIUS ontology-based BIM (Building Information Modelling) service has been elaborated by experts of Technische Hochschule Nürnberg (THN, the project' Partner) and presented to Consortium Members as an innovative tool helping to enhance the seamless exchange of all relevant information on the MOEEBIUS demo sites. The tool supports the streamlined deployment of the integrated MOEEBIUS solution to achieve their full impact on reducing the perceived gap between predicted and actual energy demand.

BIM

The approach underlines the benefits arising from capturing contextual information on buildings and sites to enable and support novel solutions such as the MOEEBIUS solution to deeply grasp and describe real-life building operation complexities in accurate simulation predictions that significantly reduce the "performance gap".

The data is gathered from all pilot sites, converted to its formal ontology-based representation and uploaded to the MOEEBIUS ontology-based BIM. – ‘We plan to expand the hosted knowledge on available points and equipment to all sites and buildings in the next weeks and will contact respective pilot partners on this behalf’ - explained Georg Ferdinand Schneider, MSc , expert of Technische Hochschule Nürnberg (University of applied sciences Nuremberg).

 

 

 

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Monday, November 19, 2018

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EU  This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680517.