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Home Sito interno Archivio BigItaly focus - Inglese

Cofely and low-consumption underground stations

it@withub.it by it@withub.it
14 Febbraio 2012
in Archivio BigItaly focus - Inglese
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(Feb 14) – Cofely, part of the Energy Services branch of the GDF Suez Group, is one the most proficient and complete answer to the major energetic and environmental challenges.
They recently started a collaboration with the Marche Polytechnic University and seven other European partners to develop a ‘Sustainable Energy Management for Underground Stations’, called Seam4Us. It is a model for monitoring and controlling energy consumption of metropolitan’s secondary stations (ventilation, lighting and auxiliary internal transports).
The development program is promoted by the European Union under the Seventh Framework Programme 2011, subprogram “Energy Efficient ICT Technology “, to fund efficiency projects for public buildings. The project Seam4Us will last 36 months and uses the underground station of ‘Paseo de Gracia’ in Barcelona to study how to reduce energy consumption by 5-10% per year (which is 63.1 million kWh), equal to the consumption of 700 homes, through the use of new technologies and innovative management methods. (Caf)

PIONEERS OF UNDERGROUND ENERGY-SAVING
The importance and innovation of Seam4Us consists in facing, in a new way, how to save energy in underground stations. It’s the first time, in fact, that is done a complete analysis of all the features – interior and exterior – of one station building, which could affect energy consumption up to 30%. The system will be able to automatically manage the entire infrastructure with a complex network of sensors connected to a server, equipped with softwares specifically developed to collect and process data in order to optimize the energy consumption.
As example, the data will be processed for the automatic lightning control based on the presence or absence of people in the subway station or for the management of the ventilation system according to environmental conditions, crowding and passage of trains. Furthermore, the system will be able to ‘learn’, with specific functions, structure and behavior of users in the various possible terms of use, anticipating the corrective actions necessary to achieve the best results.

 

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