空调系统负荷预测方法比较分析

Comparative analysis of air conditioning load prediction methods

预测负荷对设备的优化控制意义重大,利用杭州某医院手术室空调机组的用电负荷,分别采用改进的季节性指数平滑法和径向基神经网络方法进行负荷预测研究。结果表明,对变化缺乏规律的负荷进行预测时,径向基神经网络方法的预测精度略高于改进的季节性指数平滑法。

Load prediction is very important for optimal control of equipment. This methods for carrying out load prediction of the hospital＇s operating room： Seasonal paper selects two predictive Exponential Average （SEWMA）, and Radial Basis Function （RBF） neural network, the result is that, RBF neural accurate than SEWMA to predict the load varies irregularly.