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.