Ontological Annotations, Semantic Artefacts, and Knowledge-Based Systems for the Digitalisation of the Photovoltaic and Concentrated Solar Power Sectors

Abstract

The solar energy sector is undergoing rapid expansion driven by falling technology costs, supportive policy frameworks, and growing global demand for decarbonised electricity. This expansion is generating vast and heterogeneous datasets spanning irradiance measurements, photovoltaic (PV) system performance records, component specifications, maintenance logs, and grid interaction data. Despite this abundance, solar energy data remain largely fragmented, undocumented, and inaccessible to automated reasoning systems. This article addresses the challenges faced by solar energy domain experts in converting raw data into structured, machine-interpretable domain knowledge. To this end, the role of knowledge engineering — encompassing ontological annotation, semantic modelling, and knowledge-based systems — is examined in the context of the digital transformation of the solar energy sector. The article synthesises foundational concepts from knowledge representation theory, including ontologies, taxonomies, controlled vocabularies, and schema languages, within a framework that is accessible to solar energy practitioners. A systematic review of the current state of the art on semantic artefacts in the solar energy domain is conducted. Key stakeholder roles, data producers, and knowledge consumers are identified, and the gaps and overlaps in existing conceptualisations are analysed. The findings reveal that, while preliminary efforts exist, the solar energy domain lacks a coherent, community-adopted ontological framework comparable to those emerging in adjacent domains such as wind energy. Recommendations for the development, publication, and maintenance of a thriving solar energy knowledge engineering ecosystem are provided.