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MOEEBIUS

Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability

Project Details

Project date: 
November, 2015 to April, 2019
Contact Person: 
Ander Romero
Tecnalia Corporación Tecnológica
Spain

Budget Information

EU contribution: 
€6,036,468
Total cost : 
€7,288,383
Funding programme: 
H2020
EeB 7 – 2015: New tools and methodologies to reduce the gap between predicted and actual energy performances at the level of buildings and blocks of buildings

Pilot Cities

  1. Mafra
  2. Belgrade
  3. London

MOEEBIUS introduces a Holistic Energy Performance Optimization Framework that enhances current modelling approaches and delivers innovative simulation tools which (i) deeply grasp and describe real-life building operation complexities in accurate simulation predictions that significantly reduce the “performance gap” and, (ii) 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.

1. Advancing the capabilities of current Building and District Energy Performance Simulation Tools, to enable accurate predictions through addressing current modelling and measurement & verification inefficiencies.
2. Further optimizing the performance gap through human-centric fine grained control, predictive maintenance and retrofitting at building and district level.
3. Enabling the efficient Integration of distributed and intermittent energy resources into the Smart Grid and enhancing reliability and security of energy supply.
4. Facilitate Energy Performance Contracting penetration in EU Energy Services Markets through the provision of a replicable and easily transferable framework.
5. Introducing Novel ESCO Business Models and New Energy Market Roles enabling the transition to demand-driven Smart Grid Services through Demand Side Aggregators

1. Cloud-based management system for building or home operation optimization
2. Automated Fault Detection with prioritization and monetization of the impact of faults
3. Improved set point optimization using the models of occupant behaviour.
4. Context based Profiling Engine.
5. Data Analytics Engine
6. Behavioural Model
7. Predictive analytics and peak demand management solution
8. NOD IoT Device