Optimization of Air Conditioner Parameters Based on Dynamic Simulation and Genetic Algorithms

This paper presents an optimization method for air conditioner parameters based on dynamic simulation and genetic algorithms, aiming to reduce the time required to reach the target room temperature. A dynamic simulation model was developed to analyze room temperature changes and estimate the time to achieve the target temperature. The model integrates room thermal properties, air conditioning performance, and principles of heat transfer. We considered a specific room model, including its dimensions, air mass, external wall area, and thermal transmittance, as well as the operating parameters of the air conditioner, such as airflow and cooling differential. To optimize the geometry, inlet/outlet configuration, and airflow characteristics of the air conditioner, we constructed an objective function to minimize cooling time and set a series of geometric and airflow constraints. Using the Adaptive Inversion Genetic Algorithm (AIGA), we enhanced the global and local optimization capabilities of the standard Genetic Algorithm (GA) through adaptive crossover, mutation, and inversion operations, optimizing the design parameters of the air conditioner. Finally, we optimized the air conditioner design parameters using a genetic algorithm, successfully reducing the cooling time from 100 seconds to 51 seconds while meeting volume and airflow balance constraints. The optimized air conditioner design parameters, including dimensions and inlet/outlet configurations, significantly improved air circulation efficiency, proving the effectiveness and practical potential of genetic algorithms in solving complex multi-constraint design problems.