What-if: A causal machine learning approach to control-oriented modelling for building thermal dynamics

Operational optimization of buildings can improve efficiency, and reduce costs and emissions. This optimization typically relies on a model of the building thermal dynamics, which is used by a predictive controller to obtain optimal schedules for heating, ventilation and air conditioning. This model has historically been constructed by domain experts using physical properties of the building. However, this approach scales poorly as more and more residential and commercial buildings need to be modelled and controlled. As a consequence, researchers and practitioners have turned to data-driven models, trained only on observational data. However, this alternative is no panacea: such models often fail to generalize to truly unseen conditions due to their inability to learn cause–effect relationships - i.e. they are not causal, rather they only learn correlational associations between input and target variables. In this paper, we demonstrate this problem using classical machine learning models trained on data from two different use cases (a simulated RC building and nine real-world Dutch buildings). Our results show that, unlike commonly used data-driven methods, causal machine learning (CML) algorithms trained on debiased data can produce accurate models necessary for control-oriented applications which outperform baseline models by over 40%, besides learning the correct causal associations which we verify using a custom testing environment as well as SHAP feature analysis. These results emphasize the need to move beyond simplistic data-driven methods if control-oriented applications are to be realized in a feasible manner.