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.

 

This document contains the work relative to MOEEBIUS task “Building and District performance assessment specifications and Key Performance Indicators”.

The objective of this task is to provide the basis for the MOEEBIUS Measurement and Verification (M&V) Protocol, which defines a novel methodology through the review, combination and extension of current methodologies and protocols.

This M&V framework will enable the holistic modelling activities in the project and the definition of the MOEEBIUS local and global energy performance models. Consequently, adequate Key Performance Indicators (KPIs) have been incorporated to assess the results achieved by MOEEBIUS, that is, the optimization of the energy performance at both building and district levels, addressing energy and economic savings, but always under user’s comfort conditions. After selecting and improving existing indicators from international standards and other EU projects, the considered categories for the Key Performance Indicators will be energy, demand response, indoor environmental conditions, economic, and maintenance aspects, but also the MOEEBIUS simulation system. For each KPI, it will be defined the calculation method with the formulas used and monitoring needs to determine the performance of the parameters.

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Thursday, April 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.

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