Modeling buildings' heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents' behavior. Gray-box models offer a causal inference of those dynamics expressed in few parameters specific to built environments. These parameters can provide compelling insights into the characteristics of building artifacts and have various applications such as forecasting HVAC usage, indoor temperature control monitoring of built environments, etc. In this paper, we present a systematic study of modeling buildings' thermal characteristics and thus derive the parameters of built conditions with a Bayesian approach. We build a Bayesian state-space model that can adapt and incorporate buildings' thermal equations and propose a generalized solution that can easily adapt prior knowledge regarding the parameters. We show that a faster approximate approach using variational inference for parameter estimation can provide similar parameters as that of a more time-consuming Markov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on two datasets to understand the generative process and show that the Bayesian approach is more interpretable. We further study the effects of prior selection for the model parameters and transfer learning, where we learn parameters from one season and use them to fit the model in the other. We perform extensive evaluations on controlled and real data traces to enumerate buildings' parameter within a 95% credible interval.
翻译:建模建筑物的热动态是一个复杂的过程,取决于各种因素,包括天气、建设热能力、绝缘保护、居民行为等。灰盒模型为建筑环境所特有的几个参数中表达的这些动态提供了因果关系推断。这些参数可以对建筑文物的特点提供令人信服的洞察力,并具有各种应用,如预测HVAC的使用、建筑环境的室内温度控制监测等。在本文件中,我们对建模建筑物的热特性进行系统研究,从而以巴耶斯方式得出建筑条件的参数。我们建立了一个巴伊西亚州-空间模型,该模型可以调整和纳入建筑物的热方程式,并提出一种普遍的解决办法,便于调整先前关于参数的知识。我们表明,使用变异推法来进行参数估计,可以提供与更耗时的Markov链 Monte Carlo(MMC)方法相似的参数。我们对两个数据集进行了广泛的评价,以了解基因化过程,并表明巴伊西亚方法更便于解释。我们进一步研究了先前选择模型参数和转移模型的结果,并提出了一种通用的解决办法,即我们从一个季度学习到另一个时期的精确度的参数。我们从一个时期学习了多少次的参数,用它们来进行真正的研究。我们从一个时期内进行可靠的研究。