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中文摘要：
分别用径向基函数（RBF）神经网络模型和BP神经网络模型对广州市一栋办公楼和一栋图书馆在夏季不同月份的逐时冷负荷进行训练和预测，发现RBF神经网络模型预测的均方根误差和平均相对误差都仅是BP神经网络方法的64％左右．仿真结果表明，RBF神经网络具有更高的预测精度及更好的泛化能力，是建筑空调负荷预测的一种有效方法．在此基础上，构建了基于RBF神经网络的建筑逐时空调负荷智能预测软件系统．
英文摘要：
The summer hourly air conditioning loads of an office building and a library building in Guangzhou are predicted by using the radial basis function （RBF） and the back propagation （BP） neural network models, respectively. It is found that both the root mean square error and the mean relative error of the prediction based on the RBF model are about 64% of those based on the BP model. Simulated results show that the RBF neural network is effective in the prediction of air conditioning load for buildings due to its high accuracy and good generalization ability. The RBF neural network-based software for the prediction of hourly air conditioning load is finally programmed.
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