Time series distance-based methods for non-intrusive load monitoring in residential buildings

Abstract : Non-Intrusive Load Monitoring (Nilm) deals with the disaggregation of in- dividual appliances from the total load at the smart meter level. This work proposes a generic methodology using temporal sequence classification algo- rithms. It is based on a low sampling rate unlike other approaches in this domain. An innovative time series distance-based approach in the temporal classification domain is compared with a standard Nilm application based on the Hidden Markov Model algorithm (Hmm). The method is validated over a data-set of 100 houses for a duration of one year (with a 10 minutes sampling rate). A qualitative analysis of the database is also conducted, allowing to segment it into four major clusters based on discussed features.
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Contributor : Ahlame Douzal <>
Submitted on : Friday, October 2, 2015 - 3:57:43 PM
Last modification on : Thursday, October 11, 2018 - 8:48:02 AM

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  • HAL Id : hal-01208468, version 1

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Kaustav Basu, Vincent Debusschere, Ahlame Douzal-Chouakria, Seddik Bacha. Time series distance-based methods for non-intrusive load monitoring in residential buildings. Energy and Buildings, Elsevier, 2015, 96 (109–117). ⟨hal-01208468⟩

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