The Energy Performance Buildings Directive (EPBD) was issued to provide a common strategy for all European countries and to implement several actions for improving energy efficiency of buildings, responsible for 40% of energy consumption. Energy Performance Certificates are provided as a tool to evaluate the energy performance of buildings; however, costly and time-consuming controls are necessary to verify the accuracy of the set and declared data. Useful tools could be the Artificial Neural Networks (ANN), whereby it is possible to estimate the energy consumptions from specific parameters, to evaluate the accuracy of data in the energy certificates, and to identify the certificates needing accurate control. In this study, an Artificial Neural Network was developed based on approximately 6500 energy certificates (2700 are self-declaration) received by the Umbria Region (central Italy), in order to evaluate the global energy consumption of buildings from several and specific parameters reported in certificates. Data was checked in compliance with energy standards and only the correct certificates were used to train the Neural Network. The implemented Neural Network was tested with database data and a good correlation was found; in particular the energy performance calculated with the Neural Network presents an error greater than 15 kW h/m2 year with respect to the real value of global energy performance index in only 3.6% of cases. Finally, a Neural Energy Performance Index (N.E.P.I.) was defined, in order to verify the accuracy of the energy certificates; the study reported in this paper shows how the new defined index could be an important tool to identify which energy certificates require controls. A refinement of the Neural Network would allow to minimize the error and to define a N.E.P.I. index that could be used by European public administrations as a tool to perform an initial check of certificates.

An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks

BURATTI, Cinzia;BARBANERA, MARCO;PALLADINO, DOMENICO
2014

Abstract

The Energy Performance Buildings Directive (EPBD) was issued to provide a common strategy for all European countries and to implement several actions for improving energy efficiency of buildings, responsible for 40% of energy consumption. Energy Performance Certificates are provided as a tool to evaluate the energy performance of buildings; however, costly and time-consuming controls are necessary to verify the accuracy of the set and declared data. Useful tools could be the Artificial Neural Networks (ANN), whereby it is possible to estimate the energy consumptions from specific parameters, to evaluate the accuracy of data in the energy certificates, and to identify the certificates needing accurate control. In this study, an Artificial Neural Network was developed based on approximately 6500 energy certificates (2700 are self-declaration) received by the Umbria Region (central Italy), in order to evaluate the global energy consumption of buildings from several and specific parameters reported in certificates. Data was checked in compliance with energy standards and only the correct certificates were used to train the Neural Network. The implemented Neural Network was tested with database data and a good correlation was found; in particular the energy performance calculated with the Neural Network presents an error greater than 15 kW h/m2 year with respect to the real value of global energy performance index in only 3.6% of cases. Finally, a Neural Energy Performance Index (N.E.P.I.) was defined, in order to verify the accuracy of the energy certificates; the study reported in this paper shows how the new defined index could be an important tool to identify which energy certificates require controls. A refinement of the Neural Network would allow to minimize the error and to define a N.E.P.I. index that could be used by European public administrations as a tool to perform an initial check of certificates.
2014
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1215694
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