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Real-Time Data Collection and Processing of Utility Customer’s Power Usage for Improved Demand Response Control

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9860))

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Abstract

A large growth in energy demand has increased renewable energy penetration into existing power grid infrastructures, as well as spurring increased research into demand response programs. But before implementing an efficient demand response program, it is first necessary to understand the power usage behaviors of a consumer. This paper presents a real-time data acquisition system for the collection and storage of power data that will allow the study of demand response in an urban area. Demand response programs are an ideal alternative to costly energy storage and spinning reserves. Detailed power consumption data is necessary to study proper demand response programs and implement efficient control decisions. A pilot system has been implemented on the island of Oahu in Hawai’i to prove the feasibility of a data collection system in a dense urban environment. The pilot program has implemented a smart metering device that is collecting power data at a high resolution and transmitting it to a server for load forecasting analysis. The architecture of the system will be discussed as well as preliminary results and scalability of the pilot system as it relates to the implementation of the system into a large urban center.

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Authors and Affiliations

  1. 2540 Dole Street Homes Hall #302, Honolulu, HI, USA

    Shawyun Sariri, Volker Schwarzer, Dominik P. H. Kalisch, Michael Angelo & Reza Ghorbani

Authors
  1. Shawyun Sariri
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  2. Volker Schwarzer
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  3. Dominik P. H. Kalisch
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  4. Michael Angelo
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  5. Reza Ghorbani
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Corresponding author

Correspondence to Shawyun Sariri .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. MIT SENSEable City Lab, Cambridge, Massachusetts, USA

    Amin Anjomshoaa

  5. Faculty of Business and Information Technology, University of Ontario Institute of Technology (UOIT), Oshawa, Ontario, Canada

    Patrick C. K. Hung

  6. Trinity University, Plainview, Texas, USA

    Dominik Kalisch

  7. New York University, Brooklyn, New York, USA

    Stanislav Sobolevsky

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Sariri, S., Schwarzer, V., Kalisch, D.P.H., Angelo, M., Ghorbani, R. (2016). Real-Time Data Collection and Processing of Utility Customer’s Power Usage for Improved Demand Response Control. In: Hameurlain, A., et al. Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVII. Lecture Notes in Computer Science(), vol 9860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53416-8_4

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  • DOI: https://doi.org/10.1007/978-3-662-53416-8_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-53415-1

  • Online ISBN: 978-3-662-53416-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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