A microservice-based framework for exploring data selection in cross-building knowledge transfer
This work addresses domain generalization for building energy prediction, but it appears incremental as it applies known data selection methods to a specific use case.
The paper tackles the problem of domain shift in cross-building energy consumption prediction by exploring multi-source training data selection, finding that minimal building description improves generalization performance.
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.