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.