Rehman, AULie, TTVallès, BTito, SR2020-03-192020-03-192019-10-242019-10-24In 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia) (pp. 2607-2612). IEEE.978-1-7281-3520-5https://hdl.handle.net/10292/13213Energy monitoring is inevitable towards achieving energy efficiency and conservation. Load disaggregation is one of the techniques towards effective energy monitoring. In the said domain, Non-Intrusive Appliance Load Monitoring (NIALM) is an attractive method where aggregated load data are acquired from a single metering point and segregated appliance level load is estimated using effective software techniques. This paper presents a low complexity event-based NIALM technique based on supervised machine learning. In this paper, the emphasis is on the disaggregation of Air Conditioning (AC) unit and Electric Vehicle (EV) charging loads due to their high significance for the overall power grid stability improvement. A comprehensive digital simulation has been carried out to validate the performance of the proposed approach and intended appliances are aptly classified having an outcome of 97% for same Data ID and 95% for different Data ID in terms of precision, recall, and f-score performance metrics.Copyright © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Smart Meters; Event Detection; Feature Engineering; Supervised Machine Learning; Non-Intrusive Appliance Load MonitoringConference ContributionOpenAccess10.1109/ISGT-Asia.2019.8881113