论文标题
使用贝叶斯深度学习方法进行不确定性吸引的建筑能源替代模型
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models
论文作者
论文摘要
基于快速机器学习的替代模型经过训练，以模拟缓慢的高保真工程模拟模型，以加速工程设计任务。这引入了不确定性，因为代理只是原始模型的近似值。 贝叶斯方法可以量化这种不确定性，并且存在遵循贝叶斯范式的深度学习模型。这些模型，即贝叶斯神经网络和高斯过程模型，使我们能够对模型的不确定性进行预测。结果，我们可以得出不确定性感知的替代模型，这些模型可以自动怀疑造成较大仿真错误的看不见的设计样本。对于这些样本，可以查询高保真模型。这概述了贝叶斯范式如何使我们能够快速杂交但近似，缓慢但准确的模型。 在本文中，我们训练两种类型的贝叶斯模型，辍学的神经网络和随机变化高斯工艺模型，以模仿复杂的高维建筑能量性能模拟问题。替代模型处理35个建筑设计参数（输入）以估算12个不同的性能指标（输出）。我们基于两种方法，证明它们具有竞争力的准确性，并表明当10％不确定性的样本转移到高保真模型时，错误可以减少30％。
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, and deep learning models exist that follow the Bayesian paradigm. These models, namely Bayesian neural networks and Gaussian process models, enable us to give predictions together with an estimate of the model's uncertainty. As a result we can derive uncertainty-aware surrogate models that can automatically suspect unseen design samples that cause large emulation errors. For these samples, the high-fidelity model can be queried instead. This outlines how the Bayesian paradigm allows us to hybridize fast, but approximate, and slow, but accurate models. In this paper, we train two types of Bayesian models, dropout neural networks and stochastic variational Gaussian Process models, to emulate a complex high dimensional building energy performance simulation problem. The surrogate model processes 35 building design parameters (inputs) to estimate 12 different performance metrics (outputs). We benchmark both approaches, prove their accuracy to be competitive, and show that errors can be reduced by up to 30% when the 10% of samples with the highest uncertainty are transferred to the high-fidelity model.
