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  • 1School of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece.

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|February 28, 2023
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Summary

This study introduces a novel deep learning model for non-intrusive load monitoring (NILM) that disaggregates multiple appliances simultaneously. This approach reduces computational costs compared to single-appliance models.

Keywords:
KL divergenceNILMconvolution neural networksdeep learningenergy disaggregationmulti-target regressionnon-intrusive load monitoringvariational inference

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Energy Systems

Background:

  • Non-intrusive load monitoring (NILM) systems using deep learning offer accurate end-use detection.
  • Current NILM models often employ a suboptimal 'one vs. one' strategy, training separate models for each appliance.
  • This leads to high computational costs for training and inference due to numerous parameters and models.

Purpose of the Study:

  • To propose a novel, efficient NILM system capable of disaggregating multiple appliances using a single model.
  • To reduce the computational burden associated with training and deploying individual appliance models.

Main Methods:

  • Development of a multi-appliance power disaggregation model based on a multi-target regression neural network.
  • The architecture features a variational encoder with convolutional layers and shared parameters across multiple regression heads.
  • The model simultaneously estimates the individual power consumption of all target appliances from the total installation load.

Main Results:

  • The proposed multi-target regression network effectively disaggregates power consumption for multiple appliances concurrently.
  • Experimental comparisons demonstrate the model's performance against existing state-of-the-art multi- and single-target NILM approaches.
  • The single-model approach shows promise in reducing the overall complexity and cost of NILM systems.

Conclusions:

  • The novel multi-appliance NILM model offers a more efficient alternative to single-appliance strategies.
  • This approach significantly reduces training and inference costs while maintaining high accuracy.
  • The developed system advances the practical deployment of NILM technology for energy management.