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Smart Energy Management: A Computational Approach

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Big Data Analytics (BDA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10721))

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Abstract

Among the practitioners in the energy management domain, there is enormous excitement about synthesizing and benefiting from numerous technologies, including real-time monitoring, net metering, demand response, distributed generation from intermittent sources such as solar and wind, active control of power flows, enhanced storage capabilities, and micro-grids. A common theme in today’s solutions is the data-driven nature of the enabling technologies — to analyze requirements, use measurement/monitoring data to drive actuation/control, optimization, and resource management. The ability of modern sensing and IOT (Internet of Things) devices to inform us about the current state of the system and provide a timely and state-appropriate (rather than a broad, imprecise) response, backed up by analysis leads to novel solutions that are also practical and efficient.

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References

  1. Farhangi, H.: The path of the smart grid. IEEE Power Energ. Mag. 8(1), 18–28 (2010)

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  2. Giordano, V., Bossart, S.: Assessing smart grid benefits and impacts: EU and U.S. initiatives joint report EC JRC - US DOE. ISBN 978-92-79-26477-1 (pdf), ISBN 978-92-79-26478-8 (print), European Commission - JRC and US Department of Electronics, Technical Report (2012)

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Correspondence to Krithi Ramamritham .

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Ramamritham, K., Karmakar, G., Shenoy, P. (2017). Smart Energy Management: A Computational Approach. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-72413-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72412-6

  • Online ISBN: 978-3-319-72413-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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