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References from Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models. Local targets link to admitted publications; unresolved targets remain external evidence.
Tracking buildings
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Surrogate modelling for sustainable building design – a review
10.1016/j.enbuild.2019.05.057 · 2019 · External reference
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Unresolved reference
External reference
Building energy optimization: an extensive benchmark of global search algorithms
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A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings
10.1016/j.enbuild.2018.02.021 · 2018 · External reference
Sequential early-design guidance for residential single-family buildings using a probabilistic metamodel of energy consumption
10.1016/j.enbuild.2016.10.047 · 2017 · External reference
Design performance and designer preference in an interactive, data-driven conceptual building design scenario
10.1016/j.destud.2020.01.001 · 2020 · External reference
The gap between predicted and measured energy performance of buildings: a framework for investigation
10.1016/j.autcon.2014.02.009 · 2014 · External reference
A comparison of six metamodeling techniques applied to building performance simulations
10.1016/j.apenergy.2017.10.102 · 2018 · External reference
Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones
10.1016/j.apenergy.2020.114715 · 2020 · External reference
What uncertainties do we need in Bayesian deep learning for computer vision?
2017 · External reference
Deep Gaussian processes
2013 · External reference
Dropout as a Bayesian approximation: representing model uncertainty in deep learning
2016 · External reference
Gaussian processes for big data
2013 · External reference
Unresolved reference
External reference
Deep learning and process understanding for data-driven earth system science
10.1038/s41586-019-0912-1 · 2019 · External reference
Review of metamodeling techniques in support of engineering design optimization
10.1115/1.2429697 · 2007 · External reference
Simulation-based decision-making in early design stages
2015 · External reference
Deep neural networks as surrogate models for urban energy simulations
2019 · External reference
On the performance of meta-models in building design optimization
10.1016/j.apenergy.2018.04.129 · 2018 · External reference
A methodology for meta-model based optimization in building energy models
10.1016/j.enbuild.2011.12.001 · 2012 · External reference
An efficient metamodel-based method to carry out multi-objective building performance optimizations
10.1016/j.enbuild.2019.109576 · 2020 · External reference
Uncertainty analysis in building performance simulation for design support
10.1016/j.enbuild.2011.06.034 · 2011 · External reference
A review of methods to match building energy simulation models to measured data
2014 · External reference
Calibration and uncertainty analysis for computer models - a meta-model based approach for integrated building energy simulation
10.1016/j.apenergy.2012.10.031 · 2013 · External reference
Calibration of building energy models for retrofit analysis under uncertainty
10.1016/j.enbuild.2011.12.029 · 2012 · External reference
Validation of a Bayesian-based method for defining residential archetypes in urban building energy models
10.1016/j.enbuild.2016.10.050 · 2017 · External reference
Hierarchical calibration of archetypes for urban building energy modeling
10.1016/j.enbuild.2018.07.030 · 2018 · External reference
Design of computer experiments: a review
10.1016/j.compchemeng.2017.05.010 · 2017 · External reference
Application and characterization of metamodels based on artificial neural networks for building performance simulation: a systematic review
10.1016/j.enbuild.2020.109972 · 2020 · External reference
Energyplus: creating a new-generation building energy simulation program
10.1016/s0378-7788(00)00114-6 · 2001 · External reference
A review of uncertainty analysis in building energy assessment
10.1016/j.rser.2018.05.029 · 2018 · External reference
Gaussian processes in machine learning
2004 · External reference
Building simulations supporting decision making in early design–a review
10.1016/j.rser.2016.03.045 · 2016 · External reference
Variational inference: a review for statisticians
10.1080/01621459.2017.1285773 · 2017 · External reference
Understanding probabilistic sparse Gaussian process approximations
2016 · External reference
Uncertainty in deep learning
2016 · External reference
Unresolved reference
External reference
Unresolved reference
1995 · External reference
Dropout: a simple way to prevent neural networks from overfitting
2014 · External reference
Variational learning of inducing variables in sparse gaussian processes
2009 · External reference
Doubly stochastic variational inference for deep Gaussian processes
2017 · External reference
Deep Gaussian processes for biogeophysical parameter retrieval and model inversion
10.1016/j.isprsjprs.2020.04.014 · 2020 · External reference
Energyplus: energy simulation program
2000 · External reference
Unresolved reference
External reference
An analysis of transformations
10.1111/j.2517-6161.1964.tb00553.x · 1964 · External reference
Unresolved reference
External reference
Tensorflow: a system for large-scale machine learning.
2016 · External reference
Unresolved reference
External reference
Constructing large scale surrogate models from big data and artificial intelligence
10.1016/j.apenergy.2017.05.155 · 2017 · External reference
Accurate uncertainties for deep learning using calibrated regression
2018 · External reference
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
1999 · External reference
Evaluating scalable uncertainty estimation methods for deep learning based molecular property prediction
10.1021/acs.jcim.9b00975 · 2020 · External reference
Component-based building performance prediction using systems engineering and machine learning
10.1016/j.apenergy.2018.07.011 · 2017 · External reference
Adaptive sampling for building simulation surrogate model derivation using the Lola-Voronoi algorithm
2019 · External reference
