基于神经网络的中央空调粒子群优化算法研究

The research of the particle swarm optimization algorithm of the central air conditioning system based on the neural network research

根据中央空调各个设备的历史状态和耗电量等数据,建立基于神经网络的数学模型,并用粒子群优化算法对模型进行求解,得到模型输出变量数值的平均误差为1.65,还得到优化设备转速和设备状态两种情况的系统效率分别提高5.56%和10.87%。研究结果对中央空调系统节能具有实际的指导意义。

Based on the historical status and power consumption data of the air conditioning system, we establish a mathematical model based on the neural network, and use the particle swarm optimization algorithm to solve the model. The average numerical error of the output variables of the model is 1.65. The system efficiency was increased 5.56% and 10.87% respectively under the conditions of optimizing equipment and equipment state.This paper has practical implications for the research on energy conservation of the central air conditioning system.