Electrical Load Classification with Open-Set Recognition

Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as well as the ability to schedule individual loads. There are proposed Smart Plugs presented in the literature capable of such tasks. The problem, however, is that these methods lack the ability to detect if a previously unseen electrical load is connected. Misclassification of such loads presents a problem for load estimation and scheduling. Open-set recognition methods solve this problem by providing a way to detect samples not belonging to any class used during the training of the classifier. This paper evaluates the novel application of open-set recognition methods to the problem of load classification. Two approaches were examined, and both offer promising results. A Support Vector Machine based approach was first evaluated. The second, more robust method used a modified OpenMax-based algorithm to detect unseen loads.

Automated summary

The full utilization of renewable energy resources is challenging due to the changing load of the electrical grid. Demand-side management is a solution to this problem, which requires knowledge about the grid load composition and the ability to schedule individual loads. Existing Smart Plugs lack the ability to detect previously unseen electrical loads, causing problems in load estimation and scheduling. This paper evaluates the application of open-set recognition methods to address this issue, with promising results from a Support Vector Machine approach and a modified OpenMax-based algorithm.