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

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Title: An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks.
Authors: Buratti, C.1 cinzia.buratti@unipg.it, Barbanera, M.2, Palladino, D.1
Source: Applied Energy. May2014, Vol. 120, p125-132. 8p.
Subjects: Artificial neural networks, Performance evaluation, Energy consumption of buildings, Data analysis, Statistical correlation
Abstract: Highlights: [•] ANN used as a tool for evaluating energy performance of buildings. [•] Training, validation, and testing of Neural Network with real energy certificates data. [•] Global energy performance index was chosen as a target of ANN. [•] A good correlation and a minimum error was found with certificates data. [•] A new energy index was defined in order to check the energy certificates. [Copyright &y& Elsevier]
Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 94696715
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks.
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  Data: <searchLink fieldCode="AR" term="%22Buratti%2C+C%2E%22">Buratti, C.</searchLink><relatesTo>1</relatesTo><i> cinzia.buratti@unipg.it</i><br /><searchLink fieldCode="AR" term="%22Barbanera%2C+M%2E%22">Barbanera, M.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Palladino%2C+D%2E%22">Palladino, D.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Applied+Energy%22">Applied Energy</searchLink>. May2014, Vol. 120, p125-132. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption+of+buildings%22">Energy consumption of buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Highlights: [•] ANN used as a tool for evaluating energy performance of buildings. [•] Training, validation, and testing of Neural Network with real energy certificates data. [•] Global energy performance index was chosen as a target of ANN. [•] A good correlation and a minimum error was found with certificates data. [•] A new energy index was defined in order to check the energy certificates. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.apenergy.2014.01.053
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      – Code: eng
        Text: English
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      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Performance evaluation
        Type: general
      – SubjectFull: Energy consumption of buildings
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      – SubjectFull: Data analysis
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      – SubjectFull: Statistical correlation
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    Titles:
      – TitleFull: An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks.
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              Text: May2014
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              Y: 2014
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