The project
SEEDS (Self learning Energy Efficient builDings and open Spaces) with the participation of the
Ferrovial Agroman R&D department as a partner, coordinated by CEMOSA and funded by the European Commission through the 7th Research, technological development and innovation framework program, is completed, after 42 months of activity.
Optimizing Buildings
The project has developed an innovative system to
optimize the performance of buildings, isolated or in a group, in terms of energy, comfort and lifecycle costs, based on construction technologies and predictive control, self-learning and artificial intelligence techniques.
Thanks to new optimization algorithms, the SEEDS system can be used to minimize energy consumption in buildings, maintaining interior air comfort and quality. The project developed a network of wireless sensors compatible with automatic and easily deployable building maintenance systems, meaning they can be implemented in the renovation of old buildings. The system, which works in both residential and tertiary sector buildings, can also be used in the open spaces that surround them.
The following aspects are worth noting from the final results:
Installation, update and maintenance of the SEEDS system, easily and efficiently in the project's two pilot locations: part of the Stavanger university campus (Norway) and an office and parking building in Madrid. Both the system's hardware and software use wireless technology which facilities its deployment and subsequent maintenance.
Automatic generation of SEEDS software for any building thanks to the creation of a database partially based on the IFC model (data structure for construction and industry) and improved to adapt to the project needs.
Easily integrated in new buildings by updating the database and implementing the system's configuration process.
Development of a catalogue of installations based on the IFC model that includes more than 100 components, with special emphasis on those belonging to the climate control systems (heating, ventilation and air conditioning).
The system makes it possible to not have architectural information about the buildings in which it is deployed, since the self-learning techniques make it possible to learn about the performance of the buildings and of its users.
It's important to highlight that buildings represent more than 35% of energy consumption in the European Union, meaning that SEED's ability to continually optimize energy consumption using the self-learning of buildings has the potential to significantly contribute to the European target of reducing energy consumption and CO2 emissions for 2020.
The project's consortium is comprised of: CEMOSA (coordinator, Spain), Fraunhofer (Germany), Ferrovial Agroman (Spain), University of Stavanger (Norway), Salford University (United Kingdom), Cidaut (Spain), Softcrits (Spain), NSC (Germany) and FASA (Germany).
