A microservice-based framework for exploring data selection in cross-building knowledge transfer

TL;DR

This paper proposes a microservice-based framework for selecting training data from multiple sources to improve domain generalization in building energy consumption prediction, addressing the challenge of domain shift in cross-building scenarios.

Contribution

It introduces a microservice-oriented methodology for data selection to enhance cross-building generalization in energy prediction models, a novel approach for domain shift mitigation.

Findings

Minimal building descriptions improve data selection effectiveness.

Data selection enhances cross-building energy prediction accuracy.

Microservice framework supports scalable data-driven domain generalization.

Abstract

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.