Research on Intelligent Control System of Central Air Conditioning Based on BAS-PSO Optimization Algorithm
Precision air conditioning centralized systems generally operate at maximum load conditions,but the actual load demand at the supply end cannot reach the maximum value,resulting in significant energy waste.For this purpose,support vector regression ma-chine was used to reasonably predict the end load demand and an air conditioning load prediction model was constructed.Then input the obtained load prediction values into the optimization model designed by the research institute.A search model was constructed using the Tenebrio search algorithm and particle swarm optimization algorithm to intelligently control centralized air conditioning.During the re-search process,it was found that the algorithm still has limitations such as being prone to falling into local optima and low convergence accuracy.Therefore,adaptive nonlinear inertia weight coefficients and Levy flight strategy were introduced to optimize it,and a new centralized air conditioning intelligent control system was ultimately obtained.Through experimental analysis,it can be concluded that the total energy consumption generated by the model is 270.76kW,with an average energy saving rate of 27.55％.This can effectively optimize the control of the air conditioning system and achieve energy conservation and consumption reduction.
precision air conditioningparticle swarm optimization algorithmlonghorn whisker search algorithmenergy conser-vation and consumption reduction
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