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.