Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6731441 | Energy and Buildings | 2015 | 9 Pages |
Abstract
Non-intrusive load monitoring (NILM) deals with the disaggregation of individual appliances from the total load at the smart meter level. This work proposes a generic methodology using temporal sequence classification algorithms. 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 (HMM) algorithm. The method is validated over a data-set of 100 houses for a duration of 1 year (with a 10Â min sampling rate). A qualitative analysis of the database is also conducted, allowing to segment it into four major clusters based on discussed features.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Kaustav Basu, Vincent Debusschere, Ahlame Douzal-Chouakria, Seddik Bacha,