Distributed semantic repositories in smart grids

Pena, Aitor; Penya, Yoseba K.

doi:10.1109/indin.2011.6034981

Cited by 9 publications

(10 citation statements)

References 5 publications

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“…Fully exploiting the possibilities that this intelligence allows includes, for instance, advanced processing and reasoning on huge data streams (expressed in RDF triplets) as shown in [16,22]. Future works will aim to refine the distributed service discovery and determine the physical limits of the PGDIN's processing event streams.…”

Section: Discussionmentioning

confidence: 99%

“…The aforementioned OWL-DL ontology enables the PGDIN to deal with a unified domain that combines all concerning standards. In order to be able to cope with all the requirements discussed before, the PGDIN is composed of the following modules and technology interfaces (the rationale of choosing the technologies presented next has been discussed and justified in [16,[21][22][23]), as shown in Figure 1.…”

Section: System Architecturementioning

confidence: 99%

“…In this way, the PGDIN has annotated in its knowledge base the information on the asset or assets it controls. For instance, it may find (by means of SPARQL queries upon its "RDF-DB" in Figure 1 [16]) to whom is that asset connected. Subsequently, having inferred the URI of the connecting asset, it may ask in the DDS bus who is the one in charge of that asset.…”

Section: Sensing the Environmentmentioning

confidence: 99%

“…Now, the problem is how to send this message exactly to the PGDINs that can reply with the information required (i.e., the PGDINs controlling the meters under this PriST). This challenge presents the same nature as the so-called federation problem in distributed systems [16]. That is, the receiving PGDINs will eventually have to re-send the query only to a number of (again, underlying) PGDINs, before they are able to answer.…”

Section: Historical Load Energy Imbalance Billingmentioning

confidence: 99%

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Distributed Semantic Architecture for Smart Grids

et al. 2012

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The smart grid revolution demands a huge effort in redesigning and enhancing current power networks, as well as integrating emerging scenarios such as distributed generation, renewable energies or the electric vehicle. This novel situation will cause a huge flood of data that can only be handled, processed and exploited in real-time with the help of cutting-edge ICT (Information and Communication Technologies). We present here a new architecture that, contrary to the previous centralised and static model, distributes the intelligence all over the grid by means of individual intelligent nodes controlling a number of electric assets. The nodes own a profile of the standard smart grid ontology stored in the knowledge base with the inferred information about their environment in RDF triples. Since the system does not have a central registry or a service directory, the connectivity emerges from the view of the world semantically encoded by each individual intelligent node (i.e., profile + inferred information). We have described a use-case both with and without real-time requirements to illustrate and validate this novel approach.

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Section: Discussionmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

Section: Sensing the Environmentmentioning

confidence: 99%

Section: Historical Load Energy Imbalance Billingmentioning

confidence: 99%

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Distributed Semantic Architecture for Smart Grids

et al. 2012

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“…It enables powerful capabilities in controlling the level of details on information and automatically extracting, enhancing and analysing large amount of data. Several approaches with different applications of ontology were seen in power system domain that include system modelling, multi-type data management [2]- [4], big data analysis [5], [6], fault diagnosis [7], [8] and network security [9], [10].…”

Section: B Ontology Approaches For Power System Networkmentioning

confidence: 99%

Ontology-based approach for malicious behaviour detection in synchrophasor networks

Albalushi

Khan

McLaughlin

et al. 2017

2017 IEEE Power &Amp; Energy Society General Meeting

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On the application of Big Data in future large-scale intelligent Smart City installations

Girtelschmid

Steinbauer

Kumar

et al. 2014

International Journal of Pervasive Computing and Communications

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale. Design/methodology/approach -The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine. Findings -By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem. Research limitations/implications -In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work. Originality/value -The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.

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Distributed semantic repositories in smart grids

Cited by 9 publications

References 5 publications

Distributed Semantic Architecture for Smart Grids

Distributed Semantic Architecture for Smart Grids

Ontology-based approach for malicious behaviour detection in synchrophasor networks

On the application of Big Data in future large-scale intelligent Smart City installations

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