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.
Highlights
Related WorksRelevant research on smart home human-computer interaction load control has achieved preliminary results at this stage
Introduction e use of Internet of ings technology to link various types of home equipment in order to give users a combined control solution for home equipment in various modes is known as smart house [1, 2]
We have focused on the topic of electricity shortages in this study and developed a strategy to assist minimise energy use in the home, office, or any other desirable location
Summary
Relevant research on smart home human-computer interaction load control has achieved preliminary results at this stage. In [15], the authors have proposed the applicability of hybrid broadcast broadband TV to a unified smart home experience. In [17], the authors have proposed a smart home environment control man-machine interface based on asynchronous electrooculogram, aiming to provide daily assistance for patients with severe spinal cord injury (SCI). The above methods have made some progress, yet the smart home human-computer interaction load control is not accurate. Is research investigates a multiobjective smart home human-computer interface load control method to address the aforementioned issues. Research shows that the designed human-computer interaction loads control algorithm can save the electricity cost and electricity consumption of smart homes. It reduces the load so that the house achieves smart home human-computer interaction while consuming the least amount of power
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