DNN Based Approach

  • Roberto Bonfigli
  • Stefano Squartini
Part of the SpringerBriefs in Energy book series (BRIEFSENERGY)


The recent success of Deep Neural Networks (DNN) in several application scenarios drove the scientific community to employ this paradigm also for NILM. Kelly and Knottenbelt compared three alternative DNNs: in the first, they employed a convolutional layer followed by long short-term memory (LSTM) layers to estimate the disaggregated signal from the aggregate one. In the second, a denoising autoencoder composed of convolutional and fully connected layers is trained to provide a denoised signal from the aggregate one. The third network estimates the start time, the end time and the mean power demand of each appliance. The algorithms were evaluated on the UK-DALE dataset and showed superior performance with respect to the combinatorial optimization and FHMM algorithms implemented in the Non-intrusive Load Monitoring Toolkit (NILMTK).


Deep neural network Denoising autoencoder Footprint Active power Reactive power 


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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Roberto Bonfigli
    • 1
  • Stefano Squartini
    • 1
  1. 1.Department of Information EngineeringMarche Polytechnic UniversityAnconaItaly

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