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Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

Author

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  • Pascal A. Schirmer

    () (School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
    Current address: University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK.
    These authors contributed equally to this work.)

  • Iosif Mporas

    () (School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
    Current address: University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK.
    These authors contributed equally to this work.)

Abstract

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.

Suggested Citation

  • Pascal A. Schirmer & Iosif Mporas, 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation," Sustainability, MDPI, Open Access Journal, vol. 11(11), pages 1-14, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3222-:d:238734
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    References listed on IDEAS

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    1. repec:gam:jsusta:v:10:y:2018:i:4:p:1001-:d:138513 is not listed on IDEAS
    2. repec:gam:jsusta:v:11:y:2019:i:1:p:251-:d:195322 is not listed on IDEAS
    3. Yu, Biying & Tian, Yaming & Zhang, Junyi, 2015. "A dynamic active energy demand management system for evaluating the effect of policy scheme on household energy consumption behavior," Energy, Elsevier, vol. 91(C), pages 491-506.
    4. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, Open Access Journal, vol. 5(11), pages 1-21, November.
    5. repec:gam:jeners:v:12:y:2019:i:7:p:1217-:d:218124 is not listed on IDEAS
    6. Kofi Afrifa Agyeman & Sekyung Han & Soohee Han, 2015. "Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System," Energies, MDPI, Open Access Journal, vol. 8(9), pages 1-20, August.
    7. repec:gam:jeners:v:11:y:2018:i:12:p:3409-:d:188176 is not listed on IDEAS
    8. repec:gam:jeners:v:10:y:2017:i:4:p:538-:d:95934 is not listed on IDEAS
    9. repec:gam:jsusta:v:10:y:2018:i:2:p:483-:d:131442 is not listed on IDEAS
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    More about this item

    Keywords

    non-intrusive load monitoring (NILM); energy disaggregation; feature selection;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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