Generation of whole building renovation scenarios using variational autoencoders

•Proposed dimensionality reduction semi-supervised Variational Autoencoders (VAEs).•VAEs can generate scenarios for all building renovation considering TEC and LCC.•The applicability of VAEs for building energy renovation has been proven.•Significant computational time improvement is achieved with a...

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Vydáno v:	Energy and buildings Ročník 230; s. 110520
Hlavní autoři:	Sharif, Seyed Amirhosain, Hammad, Amin, Eshraghi, Pegah
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Lausanne Elsevier B.V 01.01.2021 Elsevier BV
Témata:	Air conditioning Artificial intelligence Artificial neural network Building design Building energy Buildings Computer applications Computing time Datasets Deep learning Design improvements Energy consumption Energy consumption prediction Environmental impact Feature extraction Life cycle assessment Life cycle cost Life cycle costs Life cycles Machine learning models Multiple objective analysis Optimization Renovation Simulation-based multi-objective optimization Variational autoencoders Ventilation Variational autoencoders Deep learning Energy consumption prediction Building energy Life cycle cost Renovation Artificial neural network Machine learning models Simulation-based multi-objective optimization Artificial intelligence
ISSN:	0378-7788, 1872-6178
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