Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring

Muhammad Usman Hadi, Nik Hazmi Nik Suhaimi, Abdul Basit

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
39 Downloads (Pure)


From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption. Non-intrusive load monitoring (NILM), also known as energy disaggregation, tries to decompose aggregated energy consumption data and estimate each appliance’s contribution. Recently, methodologies based on Artificial Intelligence (AI) have been proposed commonly used in these models, which can be expensive to run on a server or prohibitive when the target device has limited capabilities. AI-based models are typically computationally expensive and require a lot of storage. It is not easy to reduce the computing cost and size of a neural network without sacrificing performance. This study proposed an efficient non-parametric supervised machine learning network (ENSML) architecture with a smaller size, and a quick inference time without sacrificing performance. The proposed architecture can maximise energy disaggregation performance and predict new observations based on past ones. The results showed that employing the ENSML model considerably increased the accuracy of energy prediction in 99 percent of cases.
Original languageEnglish
Article numbere85
Issue number4
Early online date16 Jul 2022
Publication statusPublished online - 16 Jul 2022


  • NILM
  • energy disaggregation
  • ENSML model


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