Decision Support for Smart Grid: Using Reasoning to Contextualize Complex Decision Making

Balduccini, Marcello; Griffor, Edward; Huth, Michael; Vishik, Claire; Wollman, David A.; Kamongi, Patrick

doi:10.1109/mscpes.2019.8738798

Cited by 2 publications

(1 citation statement)

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“…Gruber gave meaning to ontology as "a specification of a representational conceptualization for a shared domain of discoursedefinitions of classes, relations, functions, and other objects" (Chowdhury & Zhu, 2019;Kamsu-Foguem, Abanda, Doumbouya, & Tchouanguem, 2019;Maigua-Quinteros, Perez-Nata, Fernandez-Pena, & Urrutia-Urrutia, 2018). Hence, An ontology can be assumed as a condition to demonstrate the knowledge of a specific domain be represented, described, structured and shared (Balduccini et al, 2019;Kamsu-Foguem et al, 2019) due to its ability in promoting sharing of knowledge bases, knowledge organization, and interoperability among systems (Chowdhury & Zhu, 2019).…”

Section: Ontologies To Support Information and Knowledge Sharingmentioning

confidence: 99%

A Systematic review of ontology-based approach and decision-making (DM) to improve public service delivery (PSD)

Ali

Latiff

Wahab³

2020

JBSEE

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A systematic review of the DM literature on PSD was performed with the aim to build an operational ontology-based for decision makers. Five public administration journals were screened on the subject with more than 200 articles found. 29 articles were shortlisted, categorised, summarised, and applied to outline the influential factors in DM for PSD. The result of the systematic reviews also provided a brief clarification on the requirement for the creation of a more citizen-centric and coordinated eco-system for efficient PSD underpinned by effective DM.

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Section: Ontologies To Support Information and Knowledge Sharingmentioning

confidence: 99%

A Systematic review of ontology-based approach and decision-making (DM) to improve public service delivery (PSD)

Ali

Latiff

Wahab³

2020

JBSEE

View full text Add to dashboard Cite

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Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence

2021

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Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented.

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Decision Support for Smart Grid: Using Reasoning to Contextualize Complex Decision Making

Cited by 2 publications

References 10 publications

A Systematic review of ontology-based approach and decision-making (DM) to improve public service delivery (PSD)

A Systematic review of ontology-based approach and decision-making (DM) to improve public service delivery (PSD)

Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence

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