Modeling Optimization of Energy Efficiency in Buildings for Urban Sustainability
Duration: 11/2015 – 4/2019 Funding: H2020
Partners: Tecnalia Research & Innovation, Honeywell, Hypertech Energy Labs, CORK Institute of Technology, Solintel M&P, Tyndall National Institute, TH Nürnberg, Belit – Belgrade Information Technologies, KiWi Power, Instituto de Soldaduro e Qualidade, Grindrop, Beogradske Elektrane, Municipio de Mafra, ASM – Market Research and Analysis Centre
With the increasing demand for more energy efficient buildings, the construction and energy services industries are faced with the challenge to ensure that the energy performance and savings predicted during the definition energy efficiency measures is actually achieved during operation. There is, however, significant evidence to suggest that buildings underperform, illustrating a so-called ‘performance gap’, which is attributed to a variety of causal factors related to both predicted and in-use performance, implying that predictions tend to be unrealistically low whilst actual energy performance is usually unnecessarily high.
MOEEBIUS introduces a Holistic Energy Performance Optimization 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 optimalization of building energy performance as a means to further mitigate and reduce the identified ‘performance gap’ in real-time or through retrofitting.
Almende builds software solutions targeted at providing insights relevant to retrofitting buildings with energy saving solutions. This software will analyse actual performance data of buildings to identify the "performance gap", and suggest multiple strategies that can be employed for retrofitting.
The project contributes to Almende's vision on the Energy domain, specifically when it comes to creating awareness about drivers of energy waste within a network (ie. a household or cluster of households), and presenting strategies to reduce or eliminate these drivers.