Ontology Development and Knowledge Modelling for Photovoltaic Power Stations

• Published in: 2024 5th International Conference on Computer Engineering and Application (ICCEA) pp. 1824 - 1831
• Main Authors: Wang, Shihao, Pang, Aili, Tian, Dianpei, Zhang, Junrui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.04.2024
• Subjects: data integration; Green energy; knowledge graph; Knowledge graphs; Ontologies; ontology; photovoltaic power station; Photovoltaic systems; Project management; Semantics
• ISSN: 2159-1288
• DOI: 10.1109/ICCEA62105.2024.10603725

This paper presents a comprehensive framework for the development and implementation of an ontology tailored to the domain of Photovoltaic (PV) power stations. Amidst escalating global efforts to combat climate change, PV power generation emerges as a crucial technology for achieving carbon neutrality. However, the expansion of PV power stations generates a vast amount of heterogeneous data, presenting challenges in efficient data integration and utilization. This research addresses these challenges by proposing an ontology-based knowledge modeling framework specifically designed for PV power stations, leveraging a leading graph database environment to facilitate complex queries and knowledge discovery. The primary contributions of this paper are threefold: First, it introduces a detailed methodology for ontology development, focusing on the specific needs of complex engineering projects like PV power stations. Second, it develops a comprehensive ontology that encapsulates the domain-specific knowledge, thereby laying a foundational frame-work for semantic data representation and management within this domain. Finally, a case study was conducted to validate the practical applicability of the developed ontology. This case study involved converting actual project data, provided by users in diverse files and formats, into project knowledge graphs within a deployable graph database environment. The results of this case study illustrate the enhanced data query and knowledge discovery capabilities afforded by the ontology for PV projects. This work bridges a significant gap in ontology research for PV power stations, offering new avenues for data management and application within the domain. It underscores the potential of industrial knowledge graphs in addressing complex challenges across various fields, thus advancing the field of PV project management and contributing to the broader efforts in clean energy development.