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2014 International Conference on Cloud and Autonomic Computing (ICCAC) (2014)
United Kingdom
Sept. 8, 2014 to Sept. 12, 2014
ISBN: 978-1-4799-5841-2
pp: 97-100
ABSTRACT
Global energy problems necessitate an urgent transformation of the existing electrical generation grid into a smart grid, rather than a gradual evolution. A smart grid is a real-time bi-directional communication network between end users and their utility companies which monitors power demand and manages the provisioning and transport of electricity from all generation sources. As a crucial part of this transformation, increasing numbers of smart meters generate correspondingly increasing amounts of data every day. Analyzing this data to extract insight into, and to maintain control over energy usage has become a big data problem - one which cannot be handled manually, and which requires autonomic computing solutions. In this paper, we examine electric vehicles (EVs) as a use case to investigate how to use social media, sensing data, and big data analytics to optimize smart grid management. We discuss the requirements to realize such an approach and describe an autonomic system architecture and a possible design. We believe the proposed architecture and strategy will help optimize how provisioning is performed in a smart grid, even when smart meters are not available.
INDEX TERMS
Smart grids, Media, Smart meters, Autonomic systems, Electricity, Computer architecture, Cloud computing
CITATION

Y. B. Qin, J. Housell and I. Rodero, "Cloud-Based Data Analytics Framework for Autonomic Smart Grid Management," 2014 International Conference on Cloud and Autonomic Computing (ICCAC), United Kingdom, 2014, pp. 97-100.
doi:10.1109/ICCAC.2014.39
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