基于人工神经网络的地板辐射空调系统预测控制方式
Predictive control of radiant floor air conditioning systems based on an artificial neural network
摘要：
由于地板层的热容量较大，地板辐射空调系统的调节控制必须考虑房间热惰性的影响。建立了以热泵机组为冷热源的地板辐射空调系统的预测控制模型，该模型利用RBF人工神经网络技术，根据预测出的下一时刻的房间温度值来控制热泵机组的运行时间并调节房间的温度。将此预测模型用于实验系统冬季供暖工况房间温度的调节控制，实验结果表明，房间温度的预测值与实测值比较吻合。
Abstract：
Due to the large thermal capacity of floor, it is necessary to consider the influence of indoor thermal lag in control of radiant floor air conditioning systems. Develops a predictive control model for the radiant floor air conditioning system with heat pump as cold and heat sources based on RBF artificial neural network. The model can control run time of heat pump and regulate indoor temperature according to the predicted indoor temperature at the next time. Applies the model to regulate the indoor temperature of an experimental radiant floor air conditioning system in heating period. The result shows that the predicted values of indoor temperature are very consistent with real values.
Keywords:radiantfloorairconditioningsystem,artificialneuralnetwork,radialbasisfunction(RBF),heatpump,temperatureregulation,predictivecontrol