Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Time series distance-based methods for non-intrusive load monitoring in residential buildings
Highlights The method has a very limited training period (one week for a year of results). Only time stamped data of the usage of the loads is required during training. This method offers a very limited intrusion in the private life of inhabitants. The classification method is based on time series manipulation. The work is validated on a database of 100 houses; sampling rate of 10 minute.
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 10min sampling rate). A qualitative analysis of the database is also conducted, allowing to segment it into four major clusters based on discussed features.
Time series distance-based methods for non-intrusive load monitoring in residential buildings
Highlights The method has a very limited training period (one week for a year of results). Only time stamped data of the usage of the loads is required during training. This method offers a very limited intrusion in the private life of inhabitants. The classification method is based on time series manipulation. The work is validated on a database of 100 houses; sampling rate of 10 minute.
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 10min sampling rate). A qualitative analysis of the database is also conducted, allowing to segment it into four major clusters based on discussed features.
Time series distance-based methods for non-intrusive load monitoring in residential buildings
Basu, Kaustav (Autor:in) / Debusschere, Vincent (Autor:in) / Douzal-Chouakria, Ahlame (Autor:in) / Bacha, Seddik (Autor:in)
Energy and Buildings ; 96 ; 109-117
09.03.2015
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Time series distance-based methods for non-intrusive load monitoring in residential buildings
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