Building energy optimization using surrogate model and active sampling-Reference-Cited by-同舟云学术

Building energy optimization using surrogate model and active sampling

Published:2020-10-04 Issue:6 Volume:13 Page:760-776
ISSN:1940-1493
Container-title:Journal of Building Performance Simulation
language:en
Short-container-title:Journal of Building Performance Simulation

Author:

Bamdad Keivan¹,Cholette Michael E.²^ORCID,Bell John³

Affiliation:

1. College of Engineering & Science, Victoria University, Melbourne, Australia

2. School of Mechanical, Medical & Process Engineering, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, QLD, Australia

3. School of Chemistry & Physics, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, QLD, Australia

Publisher

Informa UK Limited

Subject

Computer Science Applications,Modelling and Simulation,Building and Construction,Architecture

Link

https://www.tandfonline.com/doi/pdf/10.1080/19401493.2020.1821094

Reference60 articles.

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2. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach

3. Attia, S., M. Hamdy, L. William O'Brien, and S. Carlucci. 2013. “Computational Optimization for Zero Energy Buildings Design: Interview Results with Twenty-Eight International Experts.” In 13th Conference of International Building Performance Simulation Association, Chambéry, France.

4. Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms

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