Abdullah-Al Nahid

Work place: Electronics and Communication Engineering Discipline, Khulna University, Khulna-9208, Bangladesh

E-mail: nahid.ece.ku@gmail.com

Website:

Research Interests: Medical Image Computing, Image Processing, Computational Learning Theory

Biography

Dr. Abdullah-Al Nahid is currently working as Professor in Department of Electronics and Communication Engineering, Khulna University, Khulna, Bangladesh. He received the B.Sc. degree in Electronics and Communication Engineering from Khulna University, Khulna, Bangladesh, in 2007, the M.Sc. degree in telecommunication engineering from the Institute for the Telecommunication Research (ITR), University of South Australia (UniSA), Australia, in 2014, and the Ph.D. degree from Macquarie University, Sydney Australia, in 2018. His research interests include machine learning, biomedical image processing, data classification, and smart grid.

Author Articles
Home Occupancy Classification Using Machine Learning Techniques along with Feature Selection

By Abdullah-Al Nahid Niloy Sikder Mahmudul Hasan Abid Rafia Nishat Toma Iffat Ara Talin Lasker Ershad Ali

DOI: https://doi.org/10.5815/ijem.2022.03.04, Pub. Date: 8 Jun. 2022

Monitoring systems for electrical appliances have gained massive popularity nowadays. These frameworks can provide consumers with helpful information for energy consumption. Non-intrusive load monitoring (NILM) is the most common method for monitoring a household’s energy profile. This research presents an optimized approach for identifying load needs and improving the identification of NILM occupancy surveillance. Our study suggested implementing a dimensionality reduction algorithm, popularly known as genetic algorithm (GA) along with XGBoost, for optimized occupancy monitoring. This exclusive model can masterly anticipate the usage of appliances with a significantly reduced number of voltage-current characteristics. The proposed NILM approach pre-processed the collected data and validated the anticipation performance by comparing the outcomes with the raw dataset’s performance metrics. While reducing dimensionality from 480 to 238 features, our GA-based NILM approach accomplished the same performance score in terms of accuracy (73%), recall (81%), ROC-AUC Score (0.81), and PR-AUC Score (0.81) like the original dataset. This study demonstrates that introducing GA in NILM techniques can contribute remarkably to reduce computational complexity without compromising performance.

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