Condenser model and parameter optimization analysis based on extreme learning machine
The condenser is an important component of the air conditioning refrigeration system and plays a crucial role in the operation of the entire air conditioning system.In order to obtain a high-precision and time-saving condenser model for absorption refrigeration systems,the modeling method is proposed that combines heuristic optimization algorithms and Extreme Learning Machine(ELM).Firstly,the mathematical model of the condenser is established using an extreme learning machine,and the optimal parameter values of the extreme learning machine are determined using Particle Swarm Optimization(PSO)and Ant Colony Algorithm(ACO),respectively.Then,compare the advantages and disadvantages of the two models through error evaluation indicators.The results show that the accuracy of the two established models is within±5％.The MAPE and running time of the PSO-ELM condenser model both are low.It can be concluded that the parameters calculated by the particle swarm algorithm make the model more accurate and less time-consuming.
万方数据
维普
