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.
关键词
精密空调/粒子群算法/天牛须搜索算法/节能减耗
Key words
precision air conditioning/particle swarm optimization algorithm/longhorn whisker search algorithm/energy conser-vation and consumption reduction
维普
万方数据