Non-intrusive appliance load monitoring with bagging classifiers

Nonintrusive appliance load monitoring is an important problem class with interesting applications.Due to the diculties that arise from the application of data mining techniques to real-world datasets, we close a gap in literature and focus on robust bagging classi ers. In this work, we answer thequestion, if the recognition rate of ensemble classi ers is signi cantly better than the recognition rateof the native classi ers. We analyze two types of bagging classi ers, i.e., (1) support vector machineand nearest neighbor ensembles and (2) random forests. We compare their performance in terms ofaccuracy and robustness on a NIALM data set recorded in a eld study. The experimental analysisconcentrates on recognition rates w.r.t. various training set sizes, on the inuence of neighborhoodsizes and the numbers of decision trees in random forest ensembles. It turns out that the decisiontree ensembles belong to the best classi ers in the employed scenarios.Keywords: ensembles, NIALM, support vector machines, nearest neighbor classi cation, randomforests