Towards Reproducible Test Annotation for Cyber-Physical Energy Systems using Ontology-driven Dataspaces
For researchers in cyber-physical energy systems, this work provides a structured ontology framework to improve test reproducibility, though it is an incremental step with identified limitations.
The paper addresses the lack of reproducibility in testing cyber-physical energy systems by proposing an ontology-driven dataspace approach. It demonstrates feasibility through cross-laboratory use cases but identifies remaining semantic and metadata gaps.
Reproducibility, traceability, and transparency in testing cyber-physical energy systems are crucial for scientific advancement and cross-laboratory collaboration. Current experimentation and test documentation practices lack formal semantics, making it difficult to reproduce experiments, share data, and apply, for example, the artificial intelligence-driven analysis. A dataspace that relies on structured ontologies aims to address these gaps by providing machine-actionable descriptions. In this work, we outline an ontology-driven approach for reproducibility of cyber-physical energy systems testing and illustrate its applicability through representative cross-laboratory use cases, demonstrating feasibility while identifying remaining semantic and metadata gaps that limit reproducibility. Based on these observations, we propose an open three-viewpoint ontology framework to guide future ontology extensions.