大型商场建筑夏季冷负荷动态预测模型

Prediction model of dynamic cooling load for shopping mall building in summer

夏季建筑冷负荷的正确预测是实现大型复杂中央空调优化运行、节能降耗的关键。笔者探讨了商场建筑冷负荷的主要影响因素,确定了建筑动态冷负荷预测模型的输入,提出了夏季基于新风机组供电频率的商场顾客率间接测量方法,解决了商场内顾客量难以检测的难题。还提出了AFC-HCMAC神经网络预测模型算法,实现了大型商场建筑冷负荷的动态预测。仿真结果表明:顾客率在商场冷负荷预测中占有重要地位,在冷负荷预测模型中增加商场顾客率可显著提高预测精度;AFC-HCMAC神经网络预测算法与传统的HCMAC神经网络算法比较,可有效降低神经网络节点数,提高预测精度。

The accurate energy consumption perdition for building is critical to improve the energy efficient of the operation of the operation of large-scale central air conditioning system in summer. Firstly, the influencing factors of cooling load were identified to determine the inputs of cooling load predition model. Then, the indirect measurement method was proposed to obtain the shopper rate based on the supply frequencies of new wind-8units to identify the custom number in summer. Last, an AFC-HCMAC neural network algorithm is proposed to for dynamic cooling load prediction. The results show that compared with the traditional HCMAC algorithm, the proposed AFC-HCMAC algorithm can effectively reduce the neural network nodes and improve the prediction accuracy. The shoppers rate plays an important role in the cooling load prediction for shopping mall. Increasing shopper rate in the inputs of prediction model can significantly improve the prediction accuracy of dynamical cooling load forecasting for shopping mall.