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Title: Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids

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.

Authors:
; ;
Publication Date:
Research Org.:
City of Los Angeles Department
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1332287
Report Number(s):
DOE-USC-00192-117
Journal ID: ISSN 0302--9743
DOE Contract Number:  
OE0000192
Resource Type:
Conference
Resource Relation:
Journal Volume: 7650; Conference: International Semantic Web Conference Boston, MA, USA November 11-15, 2012
Country of Publication:
United States
Language:
English

Citation Formats

Zhou, Qunzhi, Simmhan, Yogesh, and Prasanna, Viktor. Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids. United States: N. p., 2012. Web. doi:10.1007/978-3-642-35173-0_17.
Zhou, Qunzhi, Simmhan, Yogesh, & Prasanna, Viktor. Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids. United States. doi:10.1007/978-3-642-35173-0_17.
Zhou, Qunzhi, Simmhan, Yogesh, and Prasanna, Viktor. Thu . "Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids". United States. doi:10.1007/978-3-642-35173-0_17. https://www.osti.gov/servlets/purl/1332287.
@article{osti_1332287,
title = {Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids},
author = {Zhou, Qunzhi and Simmhan, Yogesh and Prasanna, Viktor},
abstractNote = {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.},
doi = {10.1007/978-3-642-35173-0_17},
journal = {},
issn = {0302--9743},
number = ,
volume = 7650,
place = {United States},
year = {2012},
month = {11}
}

Conference:
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