Dimension reduction for NILM classification based on principle component analysis

Ram Machlev*, Dmitri Tolkachov, Yoash Levron, Yuval Beck

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number106459
JournalElectric Power Systems Research
StatePublished - Oct 2020


FundersFunder number
Israel Science Foundation
Israeli Innovation Authority60689
Israel Science Foundation2//7221


    • Classification
    • Non-intrusive load monitoring (NILM)
    • Power features
    • Principal component analysis (PCA)
    • Smart meter


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