Multiobjective Metaheuristic Load Control Algorithm for Interaction between Smart Home and Humans

Abstract

Technology has boosted electric power consumption both locally and worldwide, resulting in a substantial increase in demand for electric power. A multiobjective smart house human-computer interaction load control method is suggested to meet the goal of lowering power usage and pricing in smart home load control. It presents a model that includes marginal costs and establishes an electricity price model that takes the load rate into account. It identifies switch appliances and temperature control appliances and gathers human activities, indoor and outdoor temperature, and light intensity using intelligent equipment to create a multiparameter comfort model. It creates a multiobjective model of comfort and electricity price with the purpose of reducing electricity price and multiparameter comfort, and it improves particle swarm performance by using a distance ratio based on fitness value. The optimization algorithm solves the model, determines the best smart home human-computer interaction load control scheme, creates the smart home remote control system’s functional modules, and optimizes the multiobjective smart home human-computer interaction load control algorithm using a frequency-duration parameter tracking learning model. The testing findings suggest that the proposed algorithm can cut power prices in a reasonable manner, as well as regulating 40% reduced electricity usage and load in smart homes.