The new paradigm of smart buildings requires the fulfilment of occupants needs through the analysis of data collected within them. The building changes from activities host to provider of customized services for occupants. It makes occupation issue central and worthy of specific and detailed surveys. The digitalization supports this new approach, as the implementation of Building Management Systems (BMS) in Building Information Modelling (BIM) environment permits to link the information collected in real time from sensors to a BIM database. Users and environmental monitoring allows relating indoor activities with performance needs, varying according to comfort and energy issues. The buildings capacity to react punctually and immediately to occupants needs can be implemented with systems of sensors and actuators, that control energy, light and ventilation flows. The new research horizon is to create a cognitive building able to extract knowledge from users’ needs, through machine learning and cognitive computing systems, on how to vary its layout to maximize comfort while minimizing energy consumption, also on the basis of predictive models. The paper proposes a sensorization plan for monitoring a case study in the University of Cagliari of significant architectural value, in order to verify indoor thermohygrometric conditions and define a set of interventions compatible with the preservation of architectural features and addressed to improve its energy performance. The research aims at understanding how to organize and structure the compliance of the building, in order to anticipate users’ needs and how the centrality of these requirements influences the definition of new intervention models. The proposed workflow has been tested on a pilot building case study, the Smart Campus Demonstrator at the University of Brescia, and then a possible adaptation of the concept to the building is presented.

Preliminary definition of an energy monitoring for the implementation of a cognitive building The “Mandolesi Pavillon” at the University of Cagliari

Lavinia Chiara Tagliabue;Angelo Luigi Camillo Ciribini
2017

Abstract

The new paradigm of smart buildings requires the fulfilment of occupants needs through the analysis of data collected within them. The building changes from activities host to provider of customized services for occupants. It makes occupation issue central and worthy of specific and detailed surveys. The digitalization supports this new approach, as the implementation of Building Management Systems (BMS) in Building Information Modelling (BIM) environment permits to link the information collected in real time from sensors to a BIM database. Users and environmental monitoring allows relating indoor activities with performance needs, varying according to comfort and energy issues. The buildings capacity to react punctually and immediately to occupants needs can be implemented with systems of sensors and actuators, that control energy, light and ventilation flows. The new research horizon is to create a cognitive building able to extract knowledge from users’ needs, through machine learning and cognitive computing systems, on how to vary its layout to maximize comfort while minimizing energy consumption, also on the basis of predictive models. The paper proposes a sensorization plan for monitoring a case study in the University of Cagliari of significant architectural value, in order to verify indoor thermohygrometric conditions and define a set of interventions compatible with the preservation of architectural features and addressed to improve its energy performance. The research aims at understanding how to organize and structure the compliance of the building, in order to anticipate users’ needs and how the centrality of these requirements influences the definition of new intervention models. The proposed workflow has been tested on a pilot building case study, the Smart Campus Demonstrator at the University of Brescia, and then a possible adaptation of the concept to the building is presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11379/511377
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