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Energy Data Analytics Towards Energy-Efficient Operations for Industrial and Commercial Consumers

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Big Data Analytics (BDA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8883))

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Abstract

Zenatix Solutions Pvt. Ltd. is an energy data analytics company that helps industrial and commercial entities in optimizing their energy consumption through better understanding of their energy spend. Analysis from the high resolution energy consumption data, collected every few seconds, from smart energy meters installed at sub-load (e.g. Winding Machine, Air Handling Units (AHU), UPS system) level is presented in an easy to comprehend format for the facilities manager helping them identify inefficiencies in their daily operations and reduce their energy consumption. We present the system architecture of how high resolution energy data (and other data that may impact the energy consumption e.g. production in a manufacturing unit or weather for AHU in an IT company) is collected and managed efficiently in a cloud platform. We present several use cases of how the analysis on the collected data helped in identifying the operational inefficiencies across different customers, resulting in significant energy savings.

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References

  1. https://github.com/stevedh/readingdb

  2. Dawson-Haggerty, S., Jiang, X., Tolle, G., Ortiz, J., Culler, D.: sMAP: a simple measurement and actuation profile for physical information. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp. 197–210. ACM (2010)

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© 2014 Springer International Publishing Switzerland

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Singh, A., Bansal, V. (2014). Energy Data Analytics Towards Energy-Efficient Operations for Industrial and Commercial Consumers. In: Srinivasa, S., Mehta, S. (eds) Big Data Analytics. BDA 2014. Lecture Notes in Computer Science, vol 8883. Springer, Cham. https://doi.org/10.1007/978-3-319-13820-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-13820-6_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13819-0

  • Online ISBN: 978-3-319-13820-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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