A4 Refereed article in a conference publication

Implementation of K-nearest Neighbor on Field Programmable Gate Arrays for Appliance Classification




AuthorsAmleset Kelati, Hossam Gaber, Juha Plosila, Hannu Tenhunen

EditorsN/A

Conference nameInternational Conference on Smart Energy Grid Engineering

Publication year2020

JournalIEEE international conference on smart energy grid engineering

Book title 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE)

First page 51

Last page57

ISBN978-1-7281-9913-9

eISBN978-1-7281-9912-2

ISSN2575-2677

DOIhttps://doi.org/10.1109/SEGE49949.2020.9181975.

Web address https://ieeexplore.ieee.org/document/9181975


Abstract

Accurate appliance energy consumption information can perform with the Non-Intrusive Appliances Load Monitoring (NIALM) system. However, faster and advanced appliance classification accuracy can be enhanced by the implementation of the k-nearest neighbor (k-NN) classifier in hardware. A field-programmable gate array (FPGA) hardware implementation can speed up the processing time with a high level of performance accuracy. The result proved that the HLS-based solution has reduced design complexity and time for cost-effectiveness. The Plug Load Appliance Identification Dataset (PLAID) is used as a benchmark for the implementation. The selected appliance identification is implemented using Xilinx Zynq-7000 and the HLS-based solution has used an area of 37.1% for LUT and 21% for FF from the available chip. Thus, the implementation improved the cost and classification accuracy with a processing time of 5.9 ms and the consumed power was 1.94 W.



Last updated on 2024-26-11 at 23:41