Limits of a single surrogate model development methodology to represent housing stocks

Surrogate models for predicting housing stock thermal comfort, and operational cost, energy or emissions have increasingly gained popularity due to the potential to identify solutions to accelerate and optimise construction. In addition to the potential opportunity to progress towards climate goals and increase resource efficiencies by identifying areas of interest, they are much less time consuming than brute-force simulations of millions of scenarios. Although they are popular due to their promise, analysis of scalability limits for surrogate model development methodologies have been left out. Without this analysis, assurance that these may be used over differing housing stock scales cannot be provided. This work uses black-box model interpretation as well as common predictive performance metrics to assess the scalability of the development methodology presented. The novelty of this work stems from its comparison of the performance of a development methodology through training a learning algorithm with a progressively varied dataset to illustrate its scalability. This validates the use of a single surrogate model to represent numerous bottom-up archetypes used to represent a housing stock.