Handling imbalance in an extended PLAID

Abstract

The ability to classify appliances, given the current and voltage consumption of a household is useful for a variety of applications, including demand response verification, and eco-feedback technologies. To support research efforts in this problem domain, this paper presents an extended version of the Plug-Level Appliance Identification Dataset (PLAID), which is called PLAID 2 and contains 30 kHz voltage and current measurements of different residential appliances as they are switched on. As an extension to PLAID, this dataset adds appliance instances as well as measurements for multiple operating modes (e.g., low or high fan settings for air conditioners). As with other datasets in this problem domain, the appliance classes are not equally represented in PLAID 2. Different techniques for handling this imbalance and avoiding biasing the classifiers during training are investigated. The results indicate that performance improvement depends on the classifier type, when binary VI images are used as input.