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Prior Selection Method Using Likelihood Confidence Region and Dirichlet Process Gaussian Mixture Model for Bayesian Inference of Building Energy Models
Energy and Buildings ( IF 6.7 ) Pub Date : 2020-07-13 , DOI: 10.1016/j.enbuild.2020.110293
Dong Hyuk Yi , Deuk Woo Kim , Cheol Soo Park

It is widely acknowledged that Bayesian inference is only beneficial when prior information is properly defined. However, there is no clear rule for prior selection, and it is apparently a matter of subjective selection by the domain expert(s). In other words, because the posterior inference results can vary depending on how the prior is set, a proper definition of the prior is important in terms of objectivity and accuracy for Bayesian inference of building energy models. Hence, the authors suggest a new prior selection method using Dirichlet process Gaussian mixture model (DPGMM) and the likelihood confidence region (hereafter referred to as likelihood CR). The DPGMM is a Bayesian nonparametric clustering technique that optimizes both the cluster shape and the number of clusters. Using the DPGMM, the finite probability distributions that make up the likelihood CR can be estimated, where the distribution with the highest maximum likelihood is applied as the informative prior. In this study, a reference office building of the United States Department of Energy was selected, and a surrogate model was generated using an artificial neural network. Based on a comparison between the authors’ suggestion and traditional informative (and/or non-informative) priors by domain experts, the proposed method requires only minimum information about the parameters (min and max) and performs better than the traditional approach.



中文翻译:

利用似然置信区域和Dirichlet过程高斯混合模型进行建筑能量模型贝叶斯推理的先验选择方法

众所周知,贝叶斯推理仅在正确定义先验信息后才有用。但是,没有明确的优先选择规则,这显然是领域专家的主观选择问题。换句话说,由于后验结果会根据先验的设置方式而有所不同,因此就建筑能源模型的贝叶斯推理的客观性和准确性而言,先验的正确定义很重要。因此,作者提出了一种新的先验选择方法,该方法使用Dirichlet过程高斯混合模型(DPGMM)和似然置信区域(以下称为似然CR)进行选择。DPGMM是一种贝叶斯非参数聚类技术,可以优化聚类形状和聚类数量。使用DPGMM,可以估算出构成似然率CR的有限概率分布,其中最大似然率最高的分布用作信息先验。在这项研究中,选择了美国能源部的参考办公大楼,并使用人工神经网络生成了替代模型。根据作者的建议与领域专家的传统先验信息(和/或非信息性先验)之间的比较,所提出的方法仅需要有关参数的最小信息(最小和最大),并且性能优于传统方法。然后使用人工神经网络生成替代模型。根据作者的建议与领域专家的传统先验信息(和/或非信息性先验)之间的比较,所提出的方法仅需要有关参数的最小信息(最小和最大),并且性能优于传统方法。然后使用人工神经网络生成替代模型。根据作者的建议与领域专家的传统先验信息(和/或非信息性先验)之间的比较,所提出的方法仅需要有关参数的最小信息(最小和最大),并且性能优于传统方法。

更新日期:2020-07-13
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