Device and Time Invariant Features for Transferable Non-Intrusive Load Monitoring

Pascal Schirmer, Iosif Mporas

Research output: Contribution to journalArticlepeer-review

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Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter in a household. Although when data from the same household are used to train a disaggregation model the device disaggregation accuracy is quite high (80%-95%), depending on the number of devices, the use of pre-trained disaggregation models in new households in most cases results in a significant reduction of disaggregation accuracy. In this article we propose a transferability approach for Non-Intrusive Load Monitoring using fractional calculus and normalized Karhunen Loeve Expansion based spectrograms followed by a Convolutional Neural Network in order to generate device characteristic features that do not change significantly across different households. The performance of the proposed methodology was evaluated using two publicly available datasets, namely REDD and REFIT. The proposed transferability approach improves the Mean Absolute Error by 13.1% when compared to other transfer learning approaches for energy disaggregation.

Original languageEnglish
Pages (from-to)121-130
Number of pages10
JournalIEEE Power and Energy Technology Systems Journal
Publication statusPublished - 2 Apr 2022


  • Energy disaggregation
  • Fractional calculus
  • Karhunen Loeve expansion
  • Non-intrusive load monitoring
  • Transfer NILM
  • Transferability


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