On the relationship between sampling rate and Hidden Markov Models accuracy in non-intrusive load monitoring

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 overall 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 offering among the most accurate results. This work investigates how sensor sampling rate affects disaggregation accuracy obtained through HMM. Derived re-sampled data was passed through HMM and the resulting accuracy compared with the sampling rates. Correlation was observed and statistically verified. Two distinct groups of appliances were later identified, one which was highly correlated and another in which correlation was not observed.