期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 1, 页码 179-188出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2957517
关键词
Semantics; Linked data; Metadata; Adaptation models; Real-time systems; Information processing; Data fusion; Energy Internet (EI); linked data; semantic analysis; stream processing
类别
资金
- National Natural Science Foundation of China [61972243]
- Science and Technology Commission of Shanghai Municipality [17DZ1201502]
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.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据