Comparison of non-intrusive load monitoring supervised methods using harmonics as feature

Non-intrusive Load Monitoring (NILM), also known as energy disaggregation, is a useful technique for analyzing energy consumption data, monitored from a single-point source such as a smart meter. In this paper, a three-phase induction motor w as designed in SIM l LIN K to be used for NIL M
system based on current waveforms and odd-numbered harmonics up to the ninth harmonic. Supervised learning classifiers were proposed including decision tree, KNN, NN, Ensemble, and SV M algorithms to classify the loads with high accuracy. In comparison, results show that the decision tree
classifier can classify the loads efficiently for the most loads. Although the ensemble showed a high accuracy but still needs more time for training due to the complexity of the model. Additionally, the more samples obtained the more accuracy of classification, but a high sampling rate has more cost and analysis it takes more time for training.