In this paper, we present a new unit-level data-driven modelling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings based on easily accessible data from smart thermostats and WiFi-enabled power metres. Our physics-informed approach starts from a heat balance equation to derive a linear regression model and uses a Bayesian mixture model to identify groups of units that have similar regression coefficients. Our model captures the effect of behaviour on HC energy consumption by normalizing the effect of building characteristics and accounting for the inter-unit heat transfer and unobserved variables. Our probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data become available. Using yearly data collected in a multi-family building, our model identifies distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.

在本文中，我们提出了一种新的单元级数据驱动的建模方法，该方法可基于来自智能恒温器和启用WiFi的电表的易于访问的数据来规范多户住宅建筑中的供暖和制冷（HC）能耗。我们基于物理的方法从热平衡方程开始推导出线性回归模型，并使用贝叶斯混合模型来识别具有相似回归系数的单元组。我们的模型通过对建筑特征的影响进行归一化并考虑单元间传热和未观察到的变量来捕捉行为对 HC 能源消耗的影响。我们的概率方法结合了单位和季节特定的先验信息以及在新数据可用时对模型参数的顺序贝叶斯更新。