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International Workshop on Data Analytics for Renewable Energy Integration

DARE 2014: Data Analytics for Renewable Energy Integration pp 1–16Cite as

Towards Flexibility Detection in Device-Level Energy Consumption

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8817)

Abstract

The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.

Keywords

  • Device level analysis
  • Flexibility
  • Demand management

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  • DOI: 10.1007/978-3-319-13290-7_1
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Notes

  1. 1.

    More precisely, the expected frequency, as described in Sect. 5.4.

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Acknowledgment

This work was supported in part by the TotalFlex project sponsored by the ForskEL program of Energinet.dk.

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Authors and Affiliations

  1. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Bijay Neupane, Torben Bach Pedersen & Bo Thiesson

Authors
  1. Bijay Neupane
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  2. Torben Bach Pedersen
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  3. Bo Thiesson
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Corresponding author

Correspondence to Bijay Neupane .

Editor information

Editors and Affiliations

  1. Masdar Inst. of Science and Technology, Abu Dhabi, Utd.Arab.Emir.

    Wei Lee Woon

  2. Masdar Inst. of Science and Technology, Abu Dhabi, Utd.Arab.Emir.

    Zeyar Aung

  3. MIT Sloan School of Management, Cambridge, Massachusetts, USA

    Stuart Madnick

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© 2014 Springer International Publishing Switzerland

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Neupane, B., Pedersen, T.B., Thiesson, B. (2014). Towards Flexibility Detection in Device-Level Energy Consumption. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-13290-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13289-1

  • Online ISBN: 978-3-319-13290-7

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