Privacy-preserving data collection for demand response using self-organizing map

Kengo Okada, Kanae Matsui, Jan Haase, Hiroaki Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Homomorphic encryption for smart grids has been investigated in many studies. It is possible to estimate the total power consumption in an area without knowing the consumption data of individual households. In the case of demand response (DR), it is important to calculate the total electric power consumption in an area because DR reports are published accordingly to reduce peak power consumption when the demand is high. However, the published data may reveal private information about residents, such as the timings of specific activities (leaving from and returning home), and device details. To overcome this problem, we propose a method specialized to enable energy providers to securely share electric power consumption data. The proposed method uses a self-organizing map (SOM), which is an unsupervised learning method. In order to share power consumption data while preserving privacy, the SOM is shared without the raw data. In this framework, a target accuracy of nearly 3% is achieved, while actual data are not published by any company.

Original languageEnglish
Title of host publicationProceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages652-657
Number of pages6
ISBN (Print)9781479966493
DOIs
Publication statusPublished - 2015 Sep 28
Event13th International Conference on Industrial Informatics, INDIN 2015 - Cambridge, United Kingdom
Duration: 2015 Jul 222015 Jul 24

Other

Other13th International Conference on Industrial Informatics, INDIN 2015
CountryUnited Kingdom
CityCambridge
Period15/7/2215/7/24

Keywords

  • Data collection
  • Demand response
  • Privacy preserving
  • Self-organizing map
  • Smart grid

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Instrumentation
  • Computer Networks and Communications
  • Control and Systems Engineering

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  • Cite this

    Okada, K., Matsui, K., Haase, J., & Nishi, H. (2015). Privacy-preserving data collection for demand response using self-organizing map. In Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015 (pp. 652-657). [7281812] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INDIN.2015.7281812