ARX模型房间逐时冷负荷预测方法

Prediction method for room real-time cooling load based on ARX model

准确的冷负荷预测能减低空调能耗,对建筑节能意义重大。针对回归方法不能实时反映外部因素突变问题,提出一种实时气象因子和历史负荷为输入变量的自回归模型(ARX模型)的冷负荷预测方法。对辐射的情况进行分类,用最小二乘法辨识模型的参数,并与De ST仿真结果进行比较。实验结果表明:该方法可实现对冷负荷的逐时预测,具有良好的准确性,且简单有效。

Accurate cooling load prediction helps to reduce air-conditioning energy consumption, which is essential to building energy saving. To solve the problem that regression method is unavailable in the application to the sudden change of real-time external factors, autoregressive with exogenous （ARX） model is proposed within the input of real time meteorological factor and historical load. Depending on the radiation, the parameter identified by least square method can be compared with that simulated by DeST. The methodology is proved to predict real-time cooling load precisely, it is more simple and effective.