Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability
MOEEBIUS project is an answer to H2020-EeB-2015 call in the topic: New tools and methodologies to reduce the gap between predicted and actual energy performances at the level of buildings and blocks of buildings.
The project Consortium involves 15 Partners representing research institutions, ESCOs, SMEs, universities, public bodies from 10 countries.
MOEEBIUS introduces a Holistic Energy Performance Optimisation Framework that enhances current (passive and active building elements) 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 optimisation of building energy performance as a means to further mitigate and reduce the identified “performance gap” in real-time or through retrofitting.
MEEOBIUS also aims to promote the enhancement of customer confidence in EPC effectiveness and ESCOs ability to guarantee results and mutually agree with customers on savings targets, thus reducing business risks that have hindered the growth of the ESCO market, especially at EU 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 remain and impose further optimization challenges. To this end, MOEEBIUS introduces a multi-fold approach for iterative, short-loop and light-weight simulation-based optimization based on:
- 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). Secondly, 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 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.
Project duration: From November 2015 to April 2019.