Corpus ID: 235458132

Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring

@inproceedings{Balletti2021MixedIntegerNP,
  title={Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring},
  author={Marco Balletti and Veronica Piccialli and Antonio M. Sudoso},
  year={2021}
}
Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the energy consumption of each appliance given the aggregate signal recorded by a single smart meter. In this paper, we propose a novel two-stage optimization-based approach for energy disaggregation. In the first phase, a small training set consisting of disaggregated power profiles is used to estimate the parameters and the power states by solving a mixed integer programming… Expand

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References

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TLDR
Results demonstrate the ability of the proposed NILM algorithm to accurately identify and allocate individual energy signatures in a computationally efficient manner, which makes it suitable for inexpensive home energy management. Expand
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A graph signal processing (GSP)-based approach for non-intrusive appliance load monitoring (NILM) that aims to address the large training overhead and associated complexity of conventional graph-based methods through a novel event-based graph approach. Expand
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