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A stepwise approach integrating feature selection, regression techniques and cluster analysis to identify primary retrofit interventions on large stocks of buildings

Bibliographic Data

ID21234607
AuthorsLorenza Pistore (0000-0001-5282-4608, Free University of Bozen-Bolzano, corresponding author), Giovanni Pernigotto (0000-0002-1027-7199, Free University of Bozen-Bolzano), Francesca Cappelletti (0000-0003-2158-0449, Università Iuav di Venezia), Andrea Gasparella (0000-0001-8508-4693, Free University of Bozen-Bolzano), Piercarlo Romagnoni (0000-0002-7666-8002, Università Iuav di Venezia)
Year2019
Volume47
Pages101438
Publication date2019-05-01
Open AccessYes
TypeARTICLE
VenueSustainable Cities and Society (JOURNAL)
Journal identifiersISSN: 2210-6707 • E-ISSN: 2210-6715
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.scs.2019.101438
OpenAlexW2914839305
LanguageEN
Citations received5
References cited27

Architectural engineering · Audit · Business · Cluster analysis · Feature selection · Geography · Hierarchical clustering · Machine learning · Building Energy and Comfort Optimization · Computer Science · Engineering · Sustainable Building Design and Assessment · Urban Planning and Valuation · Accounting

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    Open Access•Leonard Kaufman, Peter J Rousseeuw•Finding Groups in Data•1990

  • Random Forests

    Open Access•Leo Breiman•Machine Learning•2001

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  • Renovation tool to improve building stock performance — Higher education context

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Citations per year0.83
Citation span2020 - 2025 (6)
Highly citedNo

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