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Endorsing domestic energy saving behavior using micro-moment classification

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  • Alsalemi, Abdullah
  • Ramadan, Mona
  • Bensaali, Faycal
  • Amira, Abbes
  • Sardianos, Christos
  • Varlamis, Iraklis
  • Dimitrakopoulos, George

Abstract

With the ever-growing rise of energy consumption and its devastating financial and environmental repercussions, it is of utmost significance to moderate energy usage with proper energy efficiency tools. This is particularly applicable to domestic energy end-users, where an accurate profile is a prerequisite for motivating energy saving behavior. This article presents an innovative method for accurately understanding domestic energy usage patterns through a classification system. It capitalizes on the emerging concept of micro-moments, short energy-related events, and builds a comprehensive profile of end-user’s energy activities with unprecedented accuracy. Micro-moments are classified based on a set of criteria per the given appliance. Five classifiers with different parameter settings were trained and tested on 10-fold cross-validated simulated data, with ensemble bagged trees topping with an accuracy of 88.0%. We also observed that linear classifiers lack in accuracy due to their inability to capture the dataset’s specific structure and patterns. Fused with the other components of our framework, the proposed classification system is a novel contribution to domestic energy profiling in an effort to step energy efficiency up to the next level.

Suggested Citation

  • Alsalemi, Abdullah & Ramadan, Mona & Bensaali, Faycal & Amira, Abbes & Sardianos, Christos & Varlamis, Iraklis & Dimitrakopoulos, George, 2019. "Endorsing domestic energy saving behavior using micro-moment classification," Applied Energy, Elsevier, vol. 250(C), pages 1302-1311.
    • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1302-1311
    DOI: 10.1016/j.apenergy.2019.05.089
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    References listed on IDEAS

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    Cited by:

    1. Varlamis, Iraklis & Sardianos, Christos & Chronis, Christos & Dimitrakopoulos, George & Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2022. "Smart fusion of sensor data and human feedback for personalized energy-saving recommendations," Applied Energy, Elsevier, vol. 305(C).
    2. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    3. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).

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