The Research And Application Of Genetic Algorithms In Energy Consumption Of Water System Of HVAC

Energy depletion and people's awareness of environmental protection make increasing energy costs. However, the fact that the depletion of energy does not change. The new substitute energy sources has not been found or large-scale use of their technology has not been a breakthrough. In this case, energy efficiency is the best compromise. In energy consumption, the proportion of building energy consumption is increasing. Moreover, in building energy consumption, the central air-conditioning energy consumption accounted for more than half of the share. Therefore, the study of energy-saving central air-conditioning for improving energy efficiency has a very important significance.In this paper, the standard genetic algorithm and Bee Evolutionary Genetic Algorithm Solve mathematical model of central air conditioning equipment and in certain conditions, find the best state of the system running. Compared with the previous central air-conditioning system used multi- variable mathematical model fitting, in this paper, using polynomial fitting. According to the relationship between parametric of mathematical model of air-conditioning, we can simplify mathematical model of the device. Mathematic model of each device has independent variables. After optimization, the air conditioning system reduce energy consumption in the range of 4.5%~48.8%. The average annual energy saving rate up25%.Study on the energy consumption optimization in a central air-conditioning system based on genetic algorithm. Air conditioning load rate is different, and the total energy consumption of central air-conditioning system is different from the optimization results. The lower the load rate of air conditioning, the more obvious the total energy consumption of central air conditioning system.Compared to the standard genetic algorithm and Bee Evolutionary Genetic Algorithm Optimization of water system in central air conditioning system, system energy consumption energy saving effect is close, but the Bee Evolutionary Genetic Algorithm for target function converges faster.