Economic operation of residential load using IOT-based renewable energy management system

The power consumed by a residential load in the world is increasing, and many methods have been adopted to reduce energy consumption. However, the methods resulted in high power loss due to untimely and non-optimal load-shifting solutions. The load-shifting strategy performed by existing methods reduces the consumer satisfaction level due to the inability to state consumers’ dynamic nature. By considering the varied requirements of the user, this paper presents an adaptive optimization that reduces overall cost and energy consumption. Therefore, an IoT-based smart data monitoring unit is adopted to note and update the energy profile and active state of each appliance in order to achieve a high level of satisfaction for each consumer. The IoT devices collect the energy profile and load device usage timing for each house. Based on shiftable, non-shiftable, and controllable load constraints, the adaptive Ebola whale (AEW) optimization provides a cost-effective solution for each user. Through optimal load shifting, the proposed optimization strategy resulted in a 40.8% cost savings for each residence. Along with that, the proposed method has improved the user comfort level by providing a minimal waiting time of 26 min. The peak-to-average ratio of the proposed method has been reduced to 14.08% and 8.69% when compared with the genetic algorithm and harmony-enhanced differential evolution, respectively.