A Stream Processing Framework Based on Linked Data for Information Collaborating of Regional Energy Networks

Coordinating of energy networks to form a city-level multidimensional integrated energy system becomes a new trend in Energy Internet (EI). The collaborating in the information layer is a core issue to achieve smart integration. However, the heterogeneity of multiagent data, the volatility of components, and the real-time analysis requirement in EI bring significant challenges. To solve these problems, in this article we propose a stream processing framework based on linked data for information collaboration among multiple energy networks. The framework provides a universal data representation based on linked data and semantic relation discovery approach to model and semantically fuse heterogeneous data. Semantics-based information transmission contracts and channels are automatically generated to adapt to structural changes in EI. A multimodel-based dynamic adjusting stream processing is implemented using data semantics. A real-world case study is implemented to demonstrate the adaptability, feasibility, and flexibility of the proposed framework.

Automated summary

This article proposes a stream processing framework based on linked data to address the challenges of information collaboration among multiple energy networks. The framework uses semantic relation discovery approach to model and fuse heterogeneous data, automatically generating semantics-based information transmission contracts and channels to adapt to structural changes in Energy Internet. The framework demonstrates adaptability, feasibility, and flexibility through a real-world case study.