Based On Chaotic Ant Colony Optimization Method Study And Software Development For Central Air-Conditioning

Air-Conditioning system energy consumption problem has been widespread attention. As the HVAC is designed under meet the user's rated load and according to rated load set-point operating mode. This is the key problem of energy consumption of a large air conditioning systems. Optimization of Air-Conditioning systems have a great significance.AHU energy consumption accounted for 35% of Air-Conditioning energy consumption. Further more, its set-point control means to cause the system work long-term operation in the large flow of the working conditions and small temperature difference. System energy efficiency is low. research on-line optimization of AHU and make the system-wide load range operation within the optimum operating point have great significance for reduce the energy consumption of Air-Conditioning system.AHU optimization goal is to minimize energy consumption when system When the system to meet the required load. This paper proposed a method for multi-constraint optimization, multi-variable, non-linear characteristics of AHU energy consumption to presents a hybrid algorithm—Chaotic Ant Colony Algorithm(CAC). It is combination of AC A and chaos optimization algorithm to solving the problem of AHU power consumption. It use the pheromones of positive feedback to guide the idea chaotic search direction and the regional. This paper used CAC into the chilled water pump and fan power of AHU in order to minimize energy consumption of AHU.Chaotic algorithm is embedded into the ant colony algorithm,fully combines the advantages of two algorithms.Through the analyse, we know that the CAC is better than the ACA to make the search capacity of the global optimal solution and its convergence has been significantly enhanced increase.the CAC is better than the traditional ant colony algorithm. Chaotic Ant Colony Optimization algorithm overcome the the phenomenon of stagnation,more easily trapped into local optimal solution and Less global search capability that the single ant colony algorithm often happen. Improve the accuracy and search speed of chaotic ant colony algorithm. For the AHU provides a new and effective ways.