IoT-Based Load Scheduling System Using Comparative Metaheuristic Optimization

Demand side management is a strategy to optimize and reduce electricity demand by urging users to alter their energy consumption patterns such that the load on the power grid is nominal. This work presents the development of a comprehensive smart energy management system for residential buildings, integrating sensors and controllers to provide full control over loads, along with a cloud-based setup that manages data logging and load schedule optimization. The proposed scheme optimizes loading schedules while reducing the Peak-to-Average Ratio and improving energy efficiency. Users are allowed to alter their optimized schedules to suit their needs, override automation when required, and view their energy consumption. This system also shows immense potential for cross-domain applications, such as in military field units. In off-grid military convoys, where diesel generators or portable batteries often provide electricity, effective energy management is crucial. The proposed scheme implements a novel objective function. It has achieved a 53 % reduction in the standard deviation, leading to a flatter load curve, ensuring better load distribution throughout the day and hence reducing pressure on the power grid. This work addresses various challenges and constraints in demand-side management, proposing an effective approach for optimized load scheduling that contributes to smarter and more efficient residential energy consumption.