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

• Published in: Journal of building performance simulation Vol. 17; no. 3; pp. 361 - 370
• Main Authors: Park, Chul-Hong, Park, Cheol Soo
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.05.2024; Taylor & Francis Ltd
• Subjects: artificial neural network; Artificial neural networks; building design; building energy retrofit; causality; Energy consumption; Retrofitting; Root-mean-square errors; Surrogate model; Workflow
• ISSN: 1940-1493; 1940-1507
• DOI: 10.1080/19401493.2023.2282078

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.