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

Kengo Okada, Kanae Matsui, Jan Haase, Hiroaki Nishi

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ652-657
ページ数6
ISBN(印刷版)9781479966493
DOI
出版ステータスPublished - 2015 9 28
イベント13th International Conference on Industrial Informatics, INDIN 2015 - Cambridge, United Kingdom
継続期間: 2015 7 222015 7 24

Other

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

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 産業および生産工学
  • 器械工学
  • コンピュータ ネットワークおよび通信
  • 制御およびシステム工学

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