Load Profile Clustering: An Algorithmic Approach With Improved Replacement in Bee Optimization Algorithm

The chief aim of this paper is to develop an effective approach to the issue of load profile clustering by applying Improved Replacement In Bee Optimization algorithm (IRIBO). While, intelligent metering solutions like Automated Meter Reading (AMR), Automated Meter Infrastructure (AMI) are in place to address the current issues prevailing in the domain of electricity markets, algorithm using Improved Replacement In Bee Optimization has been proved beneficial and uncomplicated to apply within a selective database. In this study Load Profile (LP) clustering distribution networks based on the shape of the load profile was studied for fitness function in the selected LP clustering. The results clearly indicate that LP clustering has advantages in providing metering solutions to consumers who do not possess digital metering which can be easily operated with trivial changes in the calibrations.