Interlinking Heterogeneous Data for Smart Energy Systems - O'Sullivan, Declan; Dev, Soumyabrata (Arxiv.org; Cornell University, 2019)
Smart energy systems in general, and solar energy analysis in particular, have recently gained increasing interest. This is mainly due to stronger focus on smart energy saving solutions and recent developments in photovoltaic (PV) cells. Various data-driven and machine-learning frameworks are being proposed by the research community. However, these frameworks perform their analysis- A nd are designed on-specific, heterogeneous and isolated datasets, distributed across different sites and sources, making it hard to compare results and reproduce the analysis on similar data. We propose an approach based on Web (W3C) standards and Linked Data technologies for representing and converting PV and weather records into an Resource Description Framework (RDF) graph-based data format. This format, and the presented approach, is ideal in a data integration scenario where data needs to be converted into homogeneous form and different datasets could be interlinked for distributed analysis.
IEEE International Conference on Smart Energy Systems and Technologies (SEST)
PUBLISHED
Spain
Keywords: Smart energy systems; Ontologies; Meteorology; Resource description framework; Photovoltaic systems; Semantics; Linked data; Digital Engagement; Knowledge and data engineering