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Detection of Appliance-Level Abnormal Energy Consumption in Buildings Using Autoencoders and Micro-moments

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Proceedings of the 5th International Conference on Big Data and Internet of Things (BDIoT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 489))

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Abstract

The detection of anomalous energy usage could help significantly in signaling energy wastage and identifying faulty appliances, especially if the individual power traces are analyzed. To that end, this paper proposes a novel abnormal energy consumption detection approach at the appliance-level using autoencoder and micro-moments. Accordingly, energy usage footprints of different household appliances along with occupancy patterns are analyzed for modeling normal energy consumption behaviors, and on the flip side, detecting abnormal usage. In effect, energy micro-moments occur when end-users reflexively (i) switch on/off an appliance to start/stop an energy consumption action; (ii) increase/reduce energy consumption of a specific appliance; and (iii) enter/leave a specific room. Put differently, energy micro-moments are captured by reference to end-users’ daily tasks usually performed to meet their preferences. In this regard, energy micro-moment patterns are extracted from appliance-level consumption fingerprints and occupancy data using an innovative rule-based algorithm to represent the key intent-driven moments of daily energy use, and hence model normal and abnormal behaviors. Moving forward, energy micro-moment patterns are fed into an autoencoder including 48 input/output neurons, and 4 neurons in the intermediate layer aiming at reducing the computational cost and improving the detection performance. This has helped in accurately detecting two kinds of anomalous energy consumption, i.e. “excessive consumption” and “consumption while outside”, where up to 0.95 accuracy and F1 score have been achieved, for example, when analyzing microwave energy consumption.

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Notes

  1. 1.

    https://www.thinkwithgoogle.com/marketing-strategies/micro-moments/.

  2. 2.

    (EM)\(^{3}\): Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (http://em3.qu.edu.qa/).

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Acknowledgements

This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Yassine Himeur, Abdullah Alsalemi & Faycal Bensaali

  2. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Abbes Amira

  3. Institute of Artificial Intelligence, De Montfort University, Leicester, UK

    Abbes Amira

Authors
  1. Yassine Himeur
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  2. Abdullah Alsalemi
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  3. Faycal Bensaali
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  4. Abbes Amira
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Corresponding author

Correspondence to Yassine Himeur .

Editor information

Editors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  2. UNILEHAVRE, UNIROUEN, Normandie Université, Le Havre, France

    Claude Duvallet

  3. Vrije Universiteit Brussel, Brussels, Belgium

    Abdellah Touhafi

  4. ENSA, Abdelmalek Essaâdi University, Tetuan, Morocco

    Mohammed Al Achhab

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Himeur, Y., Alsalemi, A., Bensaali, F., Amira, A. (2022). Detection of Appliance-Level Abnormal Energy Consumption in Buildings Using Autoencoders and Micro-moments. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_14

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