Objective
DREEAM aims to demonstrate replicable Net Zero Energy residential building renovation approaches achieving 75% total Net Energy Demand reduction- an improvement of 60% to state of the art market practices. It will utilise packages of interconnected energy systems that achieve a balance between energy efficiency and renewable energy measures. This is enabled by enhancing renewable technologies capacity factors by using advanced building management systems with machine learning algorithms that allow building system integration and auto-optimisation across building dimensions.
Currently, deep energy renovations mainly focus on building-level solutions, where, cost-effectively, an energy demand reduction of 40-50% can be achieved. In district scale projects, technologies for energy generation, storage and management can be integrated cost-effectively, reducing up to an additional 25-35% of Net Energy Demand. Multi-owner complexities and need for tailored system integration make this approach location-specific and hard to replicate. A focus on multi-building, single owner situations seeks the appropriate balance for both high energy demand reduction and high market replication potential. Energy Load Management technologies (ICT based building systems management) and services that are successfully piloted in commercial buildings will be adapted to enable this approach.
The approach is a breakthrough in reaching widely applicable and replicable pathways for near-NZEB residential building renovation, in particular for social and public housing, which accounts for 12% of European stock, or 25 million units.
Building owners that jointly own 160,000 dwellings, energy service engineers and finance experts collaborate to demonstrate the DREEAM approach in 3 climatologically different locations, and initiate replication in 15 more locations within the project duration. Partnership with city-, owner- and innovation networks allows dissemination to 60% of the target market.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social sciencessociologygovernance
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic zones
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- engineering and technologycivil engineeringarchitecture engineeringsustainable architecturesustainable building
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
412 96 Goteborg
Sweden
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Participants (17)
28050 Madrid
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EC2V 6EE LONDON
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31100 TREVISO TV
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261 04 LANDSKRONA
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501 15 Boras
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08013 Barcelona
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
W1G 0JD London
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1050 BRUSSELS
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00 002 Warszawa
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Participation ended
08737 TORRELLES DE FOIX
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
28016 MADRID
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WC2A 2JR London
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
12207 BERLIN
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42103 Wuppertal
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
14050 BERLIN
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08013 BARCELONA
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
217 74 Malmo
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.