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

Abstract

Artificial neural network (ANN) based surrogate models have been widely used in place of high-fidelity simulation tools. Error metrics such as mean absolute error, and root mean squared error (RMSE) have been widely used as de facto criteria. However, whether the ANN-based surrogate model can adequately reproduce the interwoven relationships and nonlinear causalities between design variables and simulated outputs are often overlooked. In this regard, the authors designed a case study regarding four ANN-based surrogate models. It was found that despite all of the models having low RMSEs, the models failed to adequately predict the causal relationships between input variables and energy use. In other words, the surrogate models were not always capable of providing accurate assessments of expected energy use reduction as a result of design changes. In this paper, we present a workflow for validating whether a surrogate model can reproduce the causal relationships between inputs and outputs.

KEYWORDS:

• Surrogate model
• artificial neural network
• causality
• building energy retrofit
• building design

Data availability statement

Due to the nature of the research, due to [ethical/legal/commercial] supporting data is not available.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport [grant number RS-2022-00141900].