Enhancement of particle swarm optimization algorithm for energy management in normal households using a mobile application

ABSTRACT: Increasing electricity costs and growing energy efficiency awareness are making household energy management a concern for households. Although much has been done around smart homes, normal homes also have a lot to offer in terms of energy consumption optimization through informed appliance scheduling and operation. This study seeks to improve the Particle Swarm Optimization (PSO) algorithm to effectively address energy management challenges in a typical residential setting by utilizing energy consumption data from Meralco, which provides detailed estimates of common appliance usage in the Philippines. The improved PSO algorithm is designed with strategies that can prevent early convergence, improve the exploration-exploitation balance, optimize particle initialization, and reduce the computational costs. These are implemented in a mobile application that aims to assist households in efficiency scheduling appliance usage based on their energy consumption patterns and cost implications. Preliminary results show that the optimization of energy usage and reducing electricity costs by the new PSO algorithm are better than the conventional algorithm, while still providing usability to the users. The findings make the enhanced PSO capable of being a real solution for energy management in any household, hence a convenient tool toward more sustainable and not-so-costly energy practices in households.

Filipiniana