Dimension reduction for NILM classification based on principle component analysis

•This paper suggests to use PCA as an efficient dimension reduction method for NILM.•The method can be used with any NILM classification technique, various datasets and sample-rates.•The method is tested using the public dataset AMPds and a private dataset.•The results show that the run time is reduced while the accuracy is preserved, i.e. there is minimal loss of information.•The suggested PCA method may be useful in applications in which run-time is an important factor, and therefore cannot use complex NILM algorithms, with a high-dimensional solution space. Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. Usually, NILM techniques are shown to be improved when various power features and additional power quality parameters are included. However, adding power features leads to increased time complexity which is a disadvantage to real-time operation. Therefore, in this work we offer a process based on principal component analysis (PCA) which reduces the dimension of NILM power features. The suggested method can be used with any NILM classification technique, and shows good performance in terms of standard measures and time complexity when tested on popular datasets.