Semantic Information Modeling for Emerging Applications in Smart Grid

Zhou, Qunzhi; Natarajan, S.; Simmhan, Yogesh; Prasanna, Viktor K.

doi:10.1109/itng.2012.150

Cited by 50 publications

(37 citation statements)

References 13 publications

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“…For events which compose domain-specific attributes, we have to provide an explicit knowledge model to capture data semantics. We have applied Semantic Web ontologies to model relevant concepts and their relationships in domains such as Smart Grid [23]. These domain ontologies are seamlessly integrated with the proposed event model and query model, as discussed later, to address data varieties.…”

Section: Figure 1: Stream Containmentmentioning

confidence: 99%

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Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Zhou

Simmhan

Prasanna

2013

2013 IEEE International Conference on Big Data

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Abstract-Emerging Big Data applications in areas like ecommerce and energy industry require both online and ondemand queries to be performed over vast and fast data arriving as streams. These present novel challenges to Big Data management systems. Complex Event Processing (CEP) is recognized as a high performance online query scheme which in particular deals with the velocity aspect of the 3-V's of Big Data. However, traditional CEP systems do not consider data variety and lack the capability to embed ad hoc queries over the volume of data streams. In this paper, we propose H2O, a stateful complex event processing framework, to support hybrid online and on-demand queries over realtime data. We propose a semantically enriched event and query model to address data variety. A formal query algebra is developed to precisely capture the stateful and containment semantics of online and on-demand queries. We describe techniques to achieve the interactive query processing over realtime data featured by efficient online querying, dynamic stream data persistence and on-demand access. The system architecture is presented and the current implementation status reported.

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Section: Figure 1: Stream Containmentmentioning

confidence: 99%

“…Particularly for the Smart Grid DR application, we organize ontologies in a modular fashion for extensibility [23].…”

Section: System Architecturementioning

confidence: 99%

Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Zhou

Simmhan

Prasanna

2013

2013 IEEE International Conference on Big Data

Self Cite

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“…Earlier, we have designed an integrated, modular Smart Grid semantic information model for dynamic DR applications [24,22]. In summary, this captures, Data Sources.…”

Section: Semantic Information Model For Smart Grid Applicationsmentioning

confidence: 99%

“…We worked with USC FMS to build the Smart Grid ontology model which captures relevant Micro Grid aspects ranging from electrical equipments, buildings, participants, and departments [24]. Using this model, asset management merely involves performing model queries that are synchronized with field operations.…”

Section: Sustainable Asset Managementmentioning

confidence: 99%

Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids

Zhou

Simmhan

Prasanna

2012

The Semantic Web – ISWC 2012

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Abstract. Semantic Web allows us to model and query time-invariant or slowly evolving knowledge using ontologies. Emerging applications in Cyber Physical Systems such as Smart Power Grids that require continuous information monitoring and integration present novel opportunities and challenges for Semantic Web technologies. Semantic Web is promising to model diverse Smart Grid domain knowledge for enhanced situation awareness and response by multi-disciplinary participants. However, current technology does pose a performance overhead for dynamic analysis of sensor measurements. In this paper, we combine semantic web and complex event processing for stream based semantic querying. We illustrate its adoption in the USC Campus Micro-Grid for detecting and enacting dynamic response strategies to peak power situations by diverse user roles. We also describe the semantic ontology and event query model that supports this. Further, we introduce and evaluate caching techniques to improve the response time for semantic event queries to meet our application needs and enable sustainable energy management.

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“…A smart grid is an advanced electricity transmission and distribution network that utilizes information, communication and control technologies to improve economy, efficiency, and security of the grid [1] [2]. We were witnesses of tremendous development and improvement of electrical distribution systems during the last decade.…”

Section: Introductionmentioning

confidence: 99%

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Kantardzic¹,

Gavranovic²,

Gavranović³

et al. 2015

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In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

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Semantic Information Modeling for Emerging Applications in Smart Grid

Cited by 50 publications

References 13 publications

Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

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