Limitations and issues of conventional artificial neural network-based surrogate models for building energy retrofit-Reference-Cited by-同舟云学术

Limitations and issues of conventional artificial neural network-based surrogate models for building energy retrofit

Published:2023-11-17 Issue: Volume: Page:1-10
ISSN:1940-1493
Container-title:Journal of Building Performance Simulation
language:en
Short-container-title:Journal of Building Performance Simulation

Author:

Park Chul-Hong¹,Park Cheol Soo²

Affiliation:

1. Department of Architecture and Architectural Engineering, College of Engineering, Seoul National University, Seoul, South Korea

2. Department of Architecture and Architectural Engineering, Institute of Construction and Environmental Engineering, Institute of Engineering Research, College of Engineering, Seoul National University, Seoul, South Korea

Funder

Korea Agency for Infrastructure Technology Advancement

Ministry of Land, Infrastructure and Transport

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/19401493.2023.2282078

Reference30 articles.

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2. Managing computational complexity using surrogate models: a critical review

3. On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design

4. Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application

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