Data-driven building energy modelling - An analysis of the potential for generalisation through interpretable machine learning

Data-driven building energy modelling techniques have proven to be effective in multiple applications. However, the debate around the possibility of generalisation is open. Generalisation involves the ability of a machine -learning model to adapt to previously unseen data and perform in a satisfactory way. Besides that, while machine-learning techniques are extremely powerful, interpretability, i.e. the ability for humans to predict how the model output will change in response to a change in input data or algorithmic parameters, is essential to attain a "human-in-the-loop" approach and creating feedback loops aimed at continuous improvement of effi-ciency measures in buildings.A flexible regression-based approach is developed and tested on a Passive House building in this study. The formulation employs dummy (binary) variables as a piecewise linearization method, and the rules for creating them are explicitly stated to ensure interpretability. Furthermore, the possibility of automating the model se-lection process using statistical indicators is described, including specific indicators used in Measurement and Verification (M&V) for the acceptance of calibrated energy models.The valuable insights that can be found using data-driven methods are reported and discussed, emphasizing limitations and constraints, as well as the potential for future research focused on systems of (interpretable data -driven) models that can exploit the techniques' spatial and temporal scalability. Finally, the physical interpre-tation of model coefficients and the analytical formulations for energy model decomposition can be used to supplement the scalability of data-driven techniques and create more sophisticated systems of interconnected models.