基于改进PSO-BP神经网络的冰蓄冷空调冷负荷动态预测模型

当前多数冰蓄冷空调冷负荷动态预测方法中，由于模型输入变量与输出结果相关性差、信息冗余度高等原因，导致多数预测模型在预测精度和收敛速度方面都未达到理想的预测效果，因此，提出一种改进的PSO-BP神经网络算法预测大型公共建筑的冷负荷。对于输入变量与输出结果采用灰色关联度分析，消除样本输入变量对数的耦合性，确定影响冰蓄冷空调系统冷负荷的关键性因素，将其作为输入变量，预测冰蓄冷空调系统动态冷负荷。结果表明：T时刻室外空气温度、T-1 h时刻室外空气温度、T时刻室外空气湿度、T时刻太阳辐射强度、T-1 h时刻太阳辐射强度、T-1 h时刻空调冷负荷是影响T时刻冰蓄冷空调系统冷负荷的关键因素，并以此作为预测模型的输入变量。相对于传统PSO-BP神经网络全输入变量预测算法，该模型预测结果精确度更高、收敛速度更快。

Dynamic load forecasting model of ice storage air conditioning based on improved PSO-BP neural network

At present, most of the dynamic prediction methods of ice storage air conditioning cooling load showed poor precision and slow convergence speed, due to bad correlation between model input variables and output results, high information redundancy. This paper proposes an improved PSO-BP neural network optimization algorithm to predict the cooling load of large public buildings. For the input variables and the output results, the degree of grey correlation analysis is used to eliminate the chance of the logarithm of the samples input variables, and the key factors affecting the cold load of the ice storage air conditioning system are determined as input variables to predict the dynamic cooling load of the ice storage air conditioning system. The results show that the key factors of the cooling load of ice storage air conditioning system include the outdoor air temperature at T h, the outdoor air temperature at T-1 h, the outdoor air humidity at T h, the solar radiation intensity at T h, the solar radiation intensity at T-1 h, and the air-conditioning cooling load at T-1 h. Therefore, these key factors act as the input variables of the prediction model which show higher accuracy and faster convergence speed than the traditional PSO-BP network model of full-variable prediction method.