Non-intrusive load monitoring using multi-label classification methods

Abstract

Non-intrusive load monitoring is a technique to help power companies monitor and analyze residential energy usage. Aggregated power load measurements for a household (i.e., the signal on the main powerline) are disaggregated into individual appliance loads by examining the appliance-specific power consumption characteristics. These data can then be used to modify consumer behaviors via detailed billing and/or demand-pricing tariffs. A number of advances in the field have been reported in the past two decades, many of which apply machine learning algorithms. However, these algorithms usually only assign one label to an example, which is a poor match to the monitoring problem, meaning elaborate encodings or classifier ensembles are needed. A more elegant solution would be to use algorithms that assign multiple labels to a single example. These multi-label classification algorithms have received very little attention in this field to date. We conduct an experimental investigation of four multi-label classification algorithms for non-intrusive monitoring and find that the best one is superior to the existing reported results on multiple real-world household datasets.