Investigation of Deep Learning-based Techniques for Load Disaggregation, Low-Frequency Approach

Investigation of Deep Learning-based Techniques for Load Disaggregation, Low-Frequency Approach

Unlike sub-metering, which requires individual appliances to be equipped with their own meters, non-intrusive load monitoring (NILM) use algorithms to discover appliance individual consumption from the aggregated overall energy reading. Approaches that uses low frequency sampled data are more applic...

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Veröffentlicht in:International journal of advanced computer science & applications 2020, Vol.11 (1)
Hauptverfasser: Alkhulaifi, Abdolmaged, J., Abdulah
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Datasets
Deep learning
Literature reviews
Machine learning
Model accuracy
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Zusammenfassung:Unlike sub-metering, which requires individual appliances to be equipped with their own meters, non-intrusive load monitoring (NILM) use algorithms to discover appliance individual consumption from the aggregated overall energy reading. Approaches that uses low frequency sampled data are more applicable in a real world smart meters that has typical sampling capability of ?
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110186