Document Type

Conference Paper


This item is available under a Creative Commons License for non-commercial use only


Computer Sciences


Providing domestic energy consumers with a detailed breakdown of their electricity consumption, at the appliance level, empowers the consumer to better manage that consumption and reduce their over- all electricity demand. Non-Intrusive Load Monitoring (NILM) is one method of achieving this breakdown and makes use of one sensor which measures overall combined electricity usage. As all appliances are measured in combination in NILM this consumption must be disaggregated to extract appliance level consumption. Machine learning techniques can be adopted to perform this disaggregation with various levels of accuracy, with Hidden Markov Model (HMM) derivatives ordering among the most accurate results. This work investigates how sensor sampling rate affects disaggregation accuracy obtained through HMM.