Building simulation in adaptive training of machine learning models
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Authors
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
Series
Automation in Construction, Volume 165
Abstract
Combining building performance simulation (BPS) and artificial intelligence (AI) provides smart buildings with the ability to adapt by utilizing BPS's data synthesis and training capabilities. There is a scarcity of comprehensive reviews focusing on how building simulation contributes to the adaptation process. The contribution of this review is to analyze the implementation of building simulation in adaptive (AI) systems as both data acquisition and training environments, by interpreting adaptation as a cyclical process. Here, the reviewed studies are classified into four major applications: prediction, optimization, control, and management. It is concluded that defining adaptation as a cyclical process provides a useful framework for the development of adaptive smart buildings. Among the reviewed control and management applications, 48% of decision-making AI agents were trained adaptively, with contributions from BPS. Further research is needed to fully exploit the potential of BPS in training decision-making AI especially when aiming at continuous (cyclical) adaptation.Description
Publisher Copyright: © 2024 The Authors
Other note
Citation
Amini, H, Alanne, K & Kosonen, R 2024, 'Building simulation in adaptive training of machine learning models', Automation in Construction, vol. 165, 105564. https://doi.org/10.1016/j.autcon.2024.105564