基于BP神经网络的印染太阳能集热温度预测

温度在印染加工最重要的工艺参数，因此对太阳能的集热能力做出及时预测是太阳能集热系统在印染行业成功应用的关键，但太阳能集热受到纵多因素的影响，单纯的数学模型难以准确预测。文中采用神经网络对太阳能集热系统进行集热温度预测，根据太阳辐照度、实测温度、天气情况、进水温度等环境参数，预测印染太阳能集热系统的集热温度。结果表明，该方法速度快，结果较准确，为印染太阳能集热控制系统的设计提供了一个新的预测手段。

Prediction of the Temperature of Solar Collector Simulation Based on BP Neural Network

Temperature is one application solar energy heating of the key parameters in printing and dyeing process, in order to system in dyeing industry successfully, prediction it＇ s collection temperature is very important, but the solar collector influenced by too many factors, a simple mathematical model is difficult to forecast accurately, therefore a neural network model was developed by the following material and environment parameters such as solar radiation, weather, average environment temperature, and initial water temperature. The result showed that it was an efficient and proper method which can serve as a new assistant means for the design of solar collector simulation of dyeing.