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.

 


MOEEBIUS objective: Further optimizing the performance gap through human-centric fine grained control, predictive maintenance and retrofitting at building and district level.


MOEEBIUS will deliver improved and enhanced models to enable more accurate energy performance predictions at building and district level. However, the “performance gap”, even though significantly reduced, will still reside and impose further optimization challenges. To this end, MOEEBIUS introduces a multi-fold approach for iterative, short-loop and light-weight simulation-based optimization on the basis of:

  • The MOEEBIUS Building level Dynamic Assessment Engine (DAE), which holds a two-fold role in the project. Firstly, the Building DAE will enable accurate Fault Detection and Diagnosis (FDD). Through fault detection and diagnosis, the Dynamic Assessment Engine will be able to recognize whether the building is beginning to operate sub-optimally and proactively identify specific performance trends (e.g. abnormal HVAC consumption gradual increase in a specific room) that could progressively lead to significant performance deviations. Subsequently, it will be able to drill-in the parameters affecting the deviating metrics (e.g. temperature trends, occupancy trends) to define the root cause of the evolving deviation. The definition of the root cause will trigger the activation of an innovative Distributed Fuzzy Model Predictive Control engine that will allow for short-term prediction of the building performance outcome under alternative automated control strategies and the mobilization of the Predictive Maintenance and Retrofitting Advisor Modules, for further investigation and definition of alternative maintenance and retrofitting actions. Resulting scenarios will be fed to the Integrated MOEEBIUS Decision Support System (MOEEBIUS-QUEST) for objectively selecting the best fitting automation/ maintenance/ retrofitting scenario or defining tailor-made strategy blends.

  • The MOEEBIUS District level Dynamic Assessment Engine, which adopts a similar approach, to allow for optimized Peak-Load Management at the district level. The District Dynamic Assessment Engine will be integrated with the MOEEBIUS DER Forecasting, Aggregation and Flexibility Analysis Module, that enables real-time formulation of Virtual Power Plants with enhanced flexibility capabilities. Optimal utilization of such flexibility in demand response schemes will allow real-time optimal coordination of distributed generation outputs, district heating systems and load flexibility for achieving high levels of peak demand reduction and optimal adaptation to renewable energy sources generation availability.

Saturday, December 16, 2017

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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.