Development of Non-Intrusive Load Monitoring of Electricity Load Classification with Low-Frequency Sampling Based on Support Vector Machine

Edi Leksono; Auditio Mandhany; Irsyad Nashirul Haq; Justin Pradipta; Putu Handre Kertha Utama; Reza Fauzi Iskandar; Rezky Mahesa Nanda

doi:10.5614/j.eng.technol.sci.2023.55.2.1

Authors

Edi Leksono Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132, Indonesia
Auditio Mandhany Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132, Indonesia
Irsyad Nashirul Haq Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132, Indonesia
Justin Pradipta Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132, Indonesia
Putu Handre Kertha Utama Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132, Indonesia
Reza Fauzi Iskandar Physics Engineering, School of Electrical Engineering, Telkom University, Jalan Telekomunikasi No.1, Bandung 40257, Indonesia
Rezky Mahesa Nanda Department of Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha No.10, Bandung 40132, Indonesia

DOI:

https://doi.org/10.5614/j.eng.technol.sci.2023.55.2.1

Keywords:

Non-Intrusive Load Monitoring, Support Vector Machine, Low Frequency Sampling, Energy Monitoring, Load Classification

Abstract

Non-intrusive load monitoring (NILM) is a promising approach to provide energy consumption monitoring of electrical appliances and analysis of current and voltage data with less instrumentation. This paper proposes an electrical load classification model using support vector machine (SVM). SVM was chosen to keep the computational cost low and be able to implement an embedded system. The SVM model was utilized to classify the on/off state of air conditioners, light bulbs, other uncategorized electronics, and their combinations. It utilizes low-frequency sampling data captured every minute, or at a 0.0167 Hz rate. Utilization change in active and reactive power was used as a feature in the model training. The optimal kernel for the model was the radial basis function (RBF) kernel with C and gamma values of 88.587 and 2.336 as hyperparameters, producing a highly accurate model. In testing with real-time conditions, the model classified the on/off state of the electrical loads with 0.93 precision, 0.91 recall, and 0.91 f-score. The results of testing proved that the model can be applied in real time with high accuracy and with an acceptable performance in field implementation using an embedded system.

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