IEEE Pervasive Computing 10 (1): Special Issue on Smart Energy Systems - part 3

Marchiori A, Hakkarinen D, Han Q, Earle L. Circuit-Level Load Monitoring for Household Energy Management. Pervasive Computing, IEEE. 2011;10(1):40-48.

This article compares a heuristic and a probabilistic classification method for steady-state features extracted from a household's aggregate power consumption. The heuristic approach used a clustering approach to train the classifier of each appliance's states. The probability mass function of each cluster was calculated, before combining all possible combinations to find the maximum joint probability. In the probabilistic approach a naive Bayesian classifier was trained using each appliance's states and state transitions. The classifier could then calculate the probability that an appliance in a given state would undergo a specific state transition. In each case, circuit level metering was used.

Although it was not the main focus of the article, it was interesting to learn that authors believe circuit-level metering allows appliances with power demands as small as 10W to be recognised. This extends the potential of NIALM beyond the recognition of appliances drawing as little as 100 or 150W as was possible through premise-level metering.

The authors describe some very promising results for the recognition of a small number of appliances tested. However, the approaches are yet to be tested in real household settings with a greater range of appliances and less usage predictability.