Building sustainable urban energy systems: The role of linked data in photovoltaic generation estimation at neighbourhood level

Abstract

The imperative of sustainable urban development demands reductions in energy consumption and carbon emissions. Solar energy emerges as a pivotal player in facilitating the vision of energy transition, serving as a significant renewable energy source for the urban sector. To advance the goals of energy transition and carbon neutrality, it is critical to comprehend the photovoltaic (PV) generation planning at the neighbourhood level, as it offers opportunities that do not exist at either the household level or city level. However, there is a lack of studies that focus on the integration of PV energy generation prediction at the neighbourhood level due to the complexity arising from the abundance of data from disparate disciplines. Supporting the estimation process for electric energy generation is important for neighbourhood level grid-resolving energy planning and management. Semantic web technologies present a promising approach to address the challenge. Through this method, we have developed the Neighbourhood Photovoltaic Generation Ontology (NPO), designed to integrate heterogeneous data to facilitate electric energy estimation processes. This approach streamlines PV energy generation estimation  and enriches the data structure by improving the interoperability of data across various formats. A case study in the Netherlands validated the methodology using monthly PV energy generation data, demonstrating that our semantic-based framework significantly enhances the estimation process. The findings demonstrate the potential of semantic web technologies for neighbourhood-level energy planning and management, offering a scalable model that can be adapted to other urban settings. Moreover, the research contributes to the body of knowledge by illustrating how linked data can be strategically support energy transition goals and carbon neutrality initiatives at the neighbourhood level.