Dynamic Air Conditioning Efficiency Model Based on Genetic Algorithm and Particle Swarm Algorithm

In this paper, for the problem of air conditioning efficiency, firstly, according to the principle of energy conservation and heat transfer, a dynamic simulation model of air conditioning efficiency is constructed, and combined with the geometrical structure of the room, the material characteristics and the air conditioning performance parameters, the dynamic equations describing the temperature changes are deduced. The dynamic regulation process of room temperature is simulated using numerical iteration method. In order to find the optimal solution under the constraints, a genetic algorithm is used to optimize the design parameters of the air conditioner, and the resulting optimization model converges within 50 iterations, stabilizing the time to reach the target temperature at about 34 seconds. Considering the dynamic nature of air conditioner efficiency, a dynamic simulation model of air purifier efficiency is constructed. The model is based on a dynamic equation describing the pollutant concentration over time, which contains the physical characteristics of the room, the air purifier design parameters, and the purification efficiency. Numerical iterations simulate the reduction process, and the optimization model is solved using the PSO algorithm. The algorithm converged within about 250 iterations and the time to reach the target concentration stabilized at 139.56 seconds.