Designing an integrated knowledge graph for smart energy services
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Designing an integrated knowledge graph for smart energy services Sejin Chun1 · Jooik Jung1 · Xiongnan Jin1 · Seungmin Seo1 · Kyong-Ho Lee1
© Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract The sharp growth of distributed energy-related resources requires an efficient energy management for future grids. The traditional power grid that highly depends on information model standards collects energy data depending on them and creates energy services. Nowadays, decentralized grids utilize information schema generated by reusing standards as well as existing schemas. The schema helps implement smart energy services in the future grids. To meet such requirements, domain experts proposed upper-level schemas that manage a wide range of energy-related knowledge resources. However, their schemas could not conduct an effective reuse of energy-related knowledge resources due to their unsuitable schema development methodologies. Moreover, there is a lack of vocabularies that satisfy critical requirements for decentralized grids. To cope with these problems, we propose an energy knowledge graph (EKG) as an upper schema for the integration of knowledge resources in energy systems. First, we utilize the existing methodology that offers guidelines for reusing existing knowledge resources. Second, EKG supports concepts of energy trading and communities to satisfy the requirements of decentralized grids. Third, EKG presents compliant concepts that are compatible with existing schemas. Fourth, we modeled the use cases using the EKG and evaluated them according to the scenario specification of energy services. Last, to demonstrate the benefits of the EKG, we implemented key components such as semantic mashup and complex event processing. Keywords Knowledge graph · Ontology · Microgrid · Smart grid · Decentralized energy resource
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Kyong-Ho Lee [email protected]
Extended author information available on the last page of the article
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S. Chun et al.
1 Introduction Future smart grids [1] encourage the shift to decentralized microgrids [2] that integrate distributed energy resources from traditional power grids, to schedule residential energy consumption and monitor system safety. The shift is due to a part of the sharp growth of energy-related resources such as distributed renewable energy resources and multi-energy systems in residential areas, smart grid unit and companies. Recent market report [3] announced that the ten largest deployments worldwide will reach 500 million smart meters by 2020. The smart meter can monitor power information from over thousands of connected devices. The increasing number of smart meters implies the explosive growth of energy-related resources in the microgrid. To counteract the proliferation of the energy-related resources, an efficient energy management is required to go toward a sustainable and resilient future smart grid. A knowledge graph (KG) [4] is known as a triple-based data model or a property model. The KGs are widely used in various modern applications in
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