Building simulation in adaptive training of machine learning models

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.