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Standards Development
Semantic Interoperability and Ontologies are presented in the IEC MSB White Paper : “Semantic interoperability: challenges in the digital transformation age” (ISBN 978-2-8322-7321-0)
Domain-based ontologies have been developed for semantic interoperability in a specific domain but the interaction of semantically equivalent objects in different ontologies has not been defined.
There have been many studies among semantic interoperability in power grid and energy ontology and different ontologies have been developed to improve energy data interoperability. However choosing a reference ontology which meets the requirement and covers the large domains in smart energy systems is a big challenge as not all ontologies represent the same energy data domains and at the same level of data details. This heterogeneity makes interoperability issues in implementation of these ontologies. Therefore, the determination of a unified Ontology is necessary for Smart Energy to go one step beyond the major innovations and improvements achieved in the past decade.
This publication provides a Guide and Plan to develop a Smart Energy Ontology and other domain-based ontologies within smart energy through semantic interoperability.
This includes but is not limited to:
• Inventory and assessment of existing ontologies for the purpose of Smart energy applications:
Reuse of existing ontologies in the smart energy domain
Evaluation of developed smart energy ontologies
Cross domain semantic interoperability support and mapping to other ontologies
• Guide and Development plan for smart energy ontology development and usage including
Definition of smart energy ontology lifecycle process
Guidance for smart energy ontology use cases
Definition of a governance process
Domain-based ontologies have been developed for semantic interoperability in a specific domain but the interaction of semantically equivalent objects in different ontologies has not been defined. This publication helps users and ontology developers to conclude the complete relationship in different domains and different ontologies for the purpose of Smart Energy applications
It should be noted that ontology itself does not contribute to reliability or flexibility; however it is an important key for system engineering and designing system data models which are required for communication between different domains. Power system may function differently in future due to economical, environment and other regulation aspects however, requirement of digital communication and data models will be generic.
Benefits of smart energy ontology
• Supports common understanding of terms used within power grid
o Terms and definitions in power grid are domain-specific and may cause misunderstanding between systems of different domains. A common language enables precise interpretation of exchanged messages and supports semantic transformation all along the product and systems life cycle.
• Supports interaction between different existing data models that are used in the Smart Energy domain
o There are required standards and standardized data models for different domain-specific systems but not a common architecture for semantic interoperability between these data models in smart energy.
• Supports the extension of domains
o The expansion of smart energy domain should not influence the ontology. Thus, smart energy ontology is designed in a modular way.
• Supports system-based designing and engineering
o The number of designing systems and the systems-of-systems in smart grid are increasing and thereby the complexity of interaction between these systems. Smart energy ontology supports manageable and less complex system designing.
There have been many studies among semantic interoperability in power grid and energy ontology and different ontologies have been developed to improve energy data interoperability. However choosing a reference ontology which meets the requirement and covers the large domains in smart energy systems is a big challenge as not all ontologies represent the same energy data domains and at the same level of data details. This heterogeneity makes interoperability issues in implementation of these ontologies. Therefore, a unified Ontology for smart energy is required for common communication and efficient implementations.
Figure 1 shows some examples of ontologies, activities and tools related to smart energy ontologies that have been developed or are under development. The figure illustrates the expansion of the number of ontologies and therefore the need for a Core Unified Ontology for Smart Energy applications.
Figure 1 Overview of Smart Energy Ontologies
This deliverable will provide for the purpose of Smart energy applications an Inventory and assessment of existing ontologies.
Domain-based ontologies have been developed for semantic interoperability in a specific domain but the interaction of semantically equivalent objects in different ontologies has not been defined. This publication helps users and ontology developers to conclude the complete relationship in different domains and different ontologies.
Figure 2 presents a process to assess existing ontologies and determine a Core Unified Ontology for Smart Energy applications. Figure 2 Assessment process for SE Ontology
The existing developed ontologies presented by ontology developers will be detected and assessed against defined requirements. Users will interact by the mean of a User Interface, which is an application for grid users, ontology engineers and ontology provider to share or use information. Assessment is an important activity of the process as it has to analyse ontologies for their qualification and the relevance of the content. Indexation considers data properties and annotations for better search and mapping result in the Core Unified Ontology. It even uses mapping for use cases and SGAM for providing full information on semantic interoperability of the data.
Use Cases for a Smart Energy Ontology are still to be defined precisely. Hereafter is a list of some generic use cases already anticipated but should not be considered as a limited list:
• Knowledge sharing – lookup, transfer (download and upload) and re-use of datasets and design patterns.
• Adaptation – possible to adopt it in real system
• Benchmarking – compare datasets and design patterns suitability for some study.
• Collaborative studies – mapping of inputs and results of several studies into a pivot format.
• Cross-domain researches – lookup of datasets and design patterns on related domains.
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