针对当前大型供冷建筑室温预测方法精度不高，难以满足空调系统节能优化控制的问题，提出基于等效热模型的递推最小二乘辨识-卡尔曼滤波(RLS-KF)室温预测方法。为了描述建筑的非稳态热工特性，通过等效电路法建立三阶的建筑热模型，选择空调冷负荷、室外温度和太阳辐射强度作为预测模型输入变量，并利用RLS算法在线辨识模型参数，同时针对单一RLS算法预测精度不高的问题，构造伪测量值，将KF算法应用于室温预测问题以提高预测精度。以广东某办公建筑供冷条件下室温为研究对象，预测结果表明：RLS-KF算法比单一的RLS算法的预测精度和稳定性大幅提高，短期室温预测性能更为优越。

For the accuracy of indoor temperature prediction method for large cooling building is not high enough to fulfill the requirement of energy optimal control for HVAC system, an equivalent thermal model of building and a recursive least squares - Kalman filtering method (RLS-KF) for indoor temperature prediction are proposed in this paper. In order to describe the unsteady state thermal characteristics of the building, a three order building thermal model is established by equivalent circuit method, and air-conditioning cooling load, ambient temperature and solar radiation intensity are selected as input variables of the model. The RLS method is used to identify the model parameters online, however, aiming at the low prediction accuracy of single RLS method, a pseudo-measurement value is constructed, and the KF algorithm is applied to the room temperature prediction problem. Taking an office building in Guangdong as an example, the results show that the prediction accuracy and stability of RLS-KF algorithm is much higher than that of a single RLS method, and the performance is better at short-term room temperature prediction.