This chapter summarizes a long list of research activities aimed at defining a method to assess the retrofit potential of school buildings, based on maintenance needs, energy-saving potential, and the life cycle cost of the retrofitted building. New concepts are introduced as the gained comfort cost (GCC) as well as new methods are suggested as a probabilistic approach to describe users’ behavior. Moreover, innovative methods as artificial neural networks have been employed to predict school buildings’ energy performances. The GCC is a new key performance indicator employed to compare different retrofit strategies, focusing on a single classroom. Furthermore, the retrofit potential is evaluated also for the whole school building, exploiting building information modelling (BIM) to collect and transfer information to the building energy model (BEM). This method to analyze energy savings associated with the retrofit of a school building is combined with a method to manage and forecast the...

Energy retrofit potential evaluation: The regione lombardia school building asset

Lavinia Chiara Tagliabue;
2020-01-01

Abstract

This chapter summarizes a long list of research activities aimed at defining a method to assess the retrofit potential of school buildings, based on maintenance needs, energy-saving potential, and the life cycle cost of the retrofitted building. New concepts are introduced as the gained comfort cost (GCC) as well as new methods are suggested as a probabilistic approach to describe users’ behavior. Moreover, innovative methods as artificial neural networks have been employed to predict school buildings’ energy performances. The GCC is a new key performance indicator employed to compare different retrofit strategies, focusing on a single classroom. Furthermore, the retrofit potential is evaluated also for the whole school building, exploiting building information modelling (BIM) to collect and transfer information to the building energy model (BEM). This method to analyze energy savings associated with the retrofit of a school building is combined with a method to manage and forecast the...
2020
Buildings for Education: A Multidisciplinary Overview of The Design of School Buildings
Springer Cham
305
315
978-3-030-33686-8
Artificial Neural Networks (ANN); Data-driven process; Energy retrofit; Geographical Information System (GIS);
Fulvio Re Cecconi, Lavinia Chiara Tagliabue, Nicola Moretti, Enrico De Angelis, Andrea Giovanni Mainini, Sebastiano Maltese
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1890505
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