Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models

Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models

Fast machine learning-based surrogate models are trained to emulate slow, high-fidelity engineering simulation models to accelerate engineering design tasks. This introduces uncertainty as the surrogate is only an approximation of the original model.Bayesian methods can quantify that uncertainty, an...

Full description

Bibliographic Details
Main Authors: Paul Westermann, Ralph Evins
Format: Article
Language:English
Published: Elsevier 2021-03-01
Series:Energy and AI
Subjects:
Surrogate modelling
Metamodel
Building performance simulation
Uncertainty
Bayesian deep learning
Gaussian Process
Online Access:http://www.sciencedirect.com/science/article/pii/S2666546820300392

Internet

http://www.sciencedirect.com/science/article/pii/S2666546820300392

Similar Items