In this paper, we introduce a real-time modelling approach to predict the heating and cooling energy consumption of each housing unit in multi-family residential buildings. We first present measured yearly heating and cooling energy use data from an actual building and introduce the eco-feedback design and associated modelling challenges. Subsequently, we present a real-time parameter learning-based modelling approach. The model has a state-space structure while state filtering and parameter estimation are simultaneously executed through particle filter with sequential Bayesian update. The housing unit-level model is coupled with a probabilistic model of the heating and cooling system by using thermostat, power metre, and mechanical system catalogue data through a Bayesian approach. The results show that the median power prediction of the model deviates less than 3.1% from measurements while the model learns seasonal parameters such as the cooling efficiency coefficient through sequential Bayesian update.

在本文中，我们引入了一种实时建模方法来预测多户住宅建筑中每个住房单元的供暖和制冷能耗。我们首先展示了来自实际建筑物的测量年度供暖和制冷能源使用数据，并介绍了生态反馈设计和相关的建模挑战。随后，我们提出了一种基于实时参数学习的建模方法。该模型具有状态空间结构，同时状态过滤和参数估计通过具有顺序贝叶斯更新的粒子过滤器同时执行。住房单元级模型通过贝叶斯方法使用恒温器、功率计和机械系统目录数据与加热和冷却系统的概率模型相结合。