TY - JOUR

T1 - Deep convolutional learning for general early design stage prediction models

AU - Singaravel, Sundaravelpandian

AU - Suykens, Johan

AU - Geyer, Philipp Florian

N1 - Funding Information: The research is funded by STG-14-00346 at KUL and by Deutsche Forschungsgemeinschaft (DFG) in the Researcher Unit 2363 “Evaluation of building design variants in early phases using adaptive levels of development” in Subproject 4 “System-based Simulation of Energy Flows.” The authors acknowledge the support by ERC Advanced Grant E-DUALITY (787960), KU Leuven C1, FWO G.088114N.

PY - 2019/10

Y1 - 2019/10

N2 - Designers rely on performance predictions to direct the design toward appropriate requirements. Machine learning (ML) models exhibit the potential for rapid and accurate predictions. Developing conventional ML models that can be generalized well in unseen design cases requires an effective feature engineering and selection. Identifying generalizable features calls for good domain knowledge by the ML model developer. Therefore, developing ML models for all design performance parameters with conventional ML will be a time-consuming and expensive process. Automation in terms of feature engineering and selection will accelerate the use of ML models in design. Deep learning models extract features from data, which aid in model generalization. In this study, we (1) evaluate the deep learning model's capability to predict the heating and cooling demand on unseen design cases and (2) obtain an understanding of extracted features. Results indicate that deep learning model generalization is similar to or better than that of a simple neural network with appropriate features. The reason for the satisfactory generalization using the deep learning model is its ability to identify similar design options within the data distribution. The results also indicate that deep learning models can filter out irrelevant features, reducing the need for feature selection.

AB - Designers rely on performance predictions to direct the design toward appropriate requirements. Machine learning (ML) models exhibit the potential for rapid and accurate predictions. Developing conventional ML models that can be generalized well in unseen design cases requires an effective feature engineering and selection. Identifying generalizable features calls for good domain knowledge by the ML model developer. Therefore, developing ML models for all design performance parameters with conventional ML will be a time-consuming and expensive process. Automation in terms of feature engineering and selection will accelerate the use of ML models in design. Deep learning models extract features from data, which aid in model generalization. In this study, we (1) evaluate the deep learning model's capability to predict the heating and cooling demand on unseen design cases and (2) obtain an understanding of extracted features. Results indicate that deep learning model generalization is similar to or better than that of a simple neural network with appropriate features. The reason for the satisfactory generalization using the deep learning model is its ability to identify similar design options within the data distribution. The results also indicate that deep learning models can filter out irrelevant features, reducing the need for feature selection.

KW - Convolutional neural network

KW - Energy predictions

KW - Feature learning

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85072221229&partnerID=8YFLogxK

U2 - 10.1016/j.aei.2019.100982

DO - 10.1016/j.aei.2019.100982

M3 - Article

AN - SCOPUS:85072221229

VL - 42

JO - Advanced engineering informatics

JF - Advanced engineering informatics

SN - 1474-0346

M1 - 100982

ER -