Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data

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TCE2019.R3.pdf(1.28 MB)
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Devlin, Michael A.
Hayes, Barry P.
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Institute of Electrical and Electronics Engineers (IEEE)
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This paper develops an approach for household appliance identification and classification of household Activities of Daily Living (ADLs) using residential smart meter data. The process of household appliance identification, i.e. decomposing a mains electricity measurement into each of its constituent individual appliances, is a very challenging classification problem. Recent advances have made deep learning a dominant approach for classification in fields such as image processing and speech recognition. This paper presents a deep learning approach based on multi-layer, feedforward neural networks that can identify common household electrical appliances from a typical household smart meter measurement. The performance of this approach is tested and validated using publicly-available smart meter data sets. The identified appliances are then mapped to household activities, or ADLs. The resulting ADL classifier can provide insights into the behaviour of the household occupants, which has a number of applications in the energy domain and in other fields.
Load identification , Non-intrusive load monitoring , Energy disaggregation , Smart metering , Appliance identification , Machine learning , Home appliances , Smart meters , Deep learning , Monitoring , Buildings , Electric variables measurement , Clustering algorithms
Devlin, M. A. and Hayes, B. P. (2019) 'Non-Intrusive Load Monitoring and Classification of Activities of Daily Living using Residential Smart Meter Data', IEEE Transactions on Consumer Electronics, In Press, doi: 10.1109/TCE.2019.2918922
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