This paper purposes a new algorithm to determine the on-off status for Non-Intrusive Load Monitoring (NILM) by using K-means Clustering Extreme Learning Machine (ELM). K-means Clustering is used in this research to cluster the current group from the load. By clustering this current, the input for ELM can be grouped by the current magnitude from wavelet process. This clustering process is done to prevent a singularity matrix that usually happens when the inversed H matrix in ELM process is done. The simulation result shows that the proposed method has a good result as Neural Network when identifying the loads status but it has a faster training and testing time. Singularity occurs when ELM is used without applying the clustering method before.

Original languageEnglish
Pages (from-to)215-220
Number of pages6
JournalInternational Review on Modelling and Simulations
Issue number4
Publication statusPublished - Aug 2018


  • ELM
  • K-means clustering
  • NILM


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