  Supervised deep learning has achieved remarkable success in various
applications. Successful machine learning application however depends on the
availability of sufficiently large amount of data. In the absence of data from
the target domain, representative data collection from multiple sources is
often needed. However, a model trained on existing multi-source data might
generalize poorly on the unseen target domain. This problem is referred to as
domain shift. In this paper, we explore the suitability of multi-source
training data selection to tackle the domain shift challenge in the context of
domain generalization. We also propose a microservice-oriented methodology for
supporting this solution. We perform our experimental study on the use case of
building energy consumption prediction. Experimental results suggest that
minimal building description is capable of improving cross-building
generalization performances when used to select energy consumption data.
