空调系统运行负荷GRNN预测模型的应用研究

以2座五星级酒店为研究对象,通过实测数据分析了运行负荷的主要影响因素,确定冷冻水温度对运行负荷的影响作用,将其引入到空调系统运行负荷的预测研究中.应用广义回归神经网络(GRNN)理论,建立了一种动态多点输出负荷模型,提出了5种输入方案,使用2座酒店的实际数据集分别进行验证.研究结果表明:冷冻水温度对实际运行负荷的预测精度有重要影响,可显著提高GRNN负荷模型的预测准确性,以前1日24 h历史负荷、预测日天气预报以及冷冻水设定温度为输入、以预测日24 h逐时负荷为输出的GRNN负荷模型,建模简单,预测性能较好,适用于实际工程应用.

Application Study of Air Conditioning System Operational Load Prediction Based on GRNN

Two five-star hotels in Sanya were selected to be studied.The main load factors of hotels were found out according to the measured data,and chilled water temperature was an important load factor which should be considered in load predition.Applying general regression neural network methodology,this paper set up a multi-outputs load prediction model,and input vector was designed to be five cases.Two real data sets from two hotels were used to test the load model.The result demonstrates that chilled water temperature plays an important role in the load prediction,and its use can significantly improve the predictive accuracy.The GRNN model,which using the last 24 hours load data,the next day weather forecastings and chilled water temperatures as the input,and the next 24 hours loads as the output,is found to be effective for load prediction,and it is simple to make in practical utilization.