An understanding of the energy performance in buildings in an entire municipality or an entire district is important for sustainable energy planning strategies that accelerate the energy renovation process in existing buildings that are not energy efficient. The methodology described in this paper is largely based on information that is already available on building stock (i.e., cartographic documentation, thematic maps, geometric data and others). Data regarding the energy performance of buildings are collected using energy audits on sample buildings, which are selected using a statistical approach. Using the tools in a GIS platform, the integration of two data sources allows for a low cost, comprehensive framework of the energy performance of buildings. This methodology was tested in a medium sized town in the Lombardy region (Italy), and the results are discussed in this paper.

A methodology for the energy performance classification of residential building stock on an urban scale

DALL'O', GIULIANO;GALANTE, ANNALISA;TORRI, MARCO
2012

Abstract

An understanding of the energy performance in buildings in an entire municipality or an entire district is important for sustainable energy planning strategies that accelerate the energy renovation process in existing buildings that are not energy efficient. The methodology described in this paper is largely based on information that is already available on building stock (i.e., cartographic documentation, thematic maps, geometric data and others). Data regarding the energy performance of buildings are collected using energy audits on sample buildings, which are selected using a statistical approach. Using the tools in a GIS platform, the integration of two data sources allows for a low cost, comprehensive framework of the energy performance of buildings. This methodology was tested in a medium sized town in the Lombardy region (Italy), and the results are discussed in this paper.
Energy certification; Energy planning; Energy retrofit; Building stock; Georeferenced database; Energy demand modelling; Monitoring of energy performance; On-site survey; Regression analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/633263
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