Title

On the Relationship Between Sampling Rate and Hidden Markov Models Accuracy in Non-intrusive Load Monitoring

Document Type

Conference Paper

Rights

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

Disciplines

Computer Sciences

Publication Details

CEUR Workshop, 2017

Abstract

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.

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