地铁冰蓄冷空调系统负荷预测研究Study on load prediction of ice storage air conditioning system in metro
陈哲,张九根,陈浩
摘要(Abstract)：
地铁的运营特点和负荷分布特点使得其应用冰蓄冷技术能够实现"削峰填谷"以及提高空调设备使用率,均衡峰谷电网负荷,节省运行费。以南京地铁2号线某地下车站通风空调系统为研究对象,将实时负荷计算结果作为神经网络的训练数据,并通过遗传算法优化神经网络的初始权值和阈值构成GA-BP算法,采用分段训练的方法,建立地铁工作日和休息日逐时负荷24 h的提前神经网络预测模型。运用BP算法和GA-BP算法对该模型进行预测。结果表明,基于分段预测的神经网络预测模型能够满足地铁冰蓄冷空调系统负荷预测的要求,并且采用GA-BP算法的预测精度更高。
关键词(KeyWords)： 冰蓄冷空调;节能;BP神经网络;遗传算法;负荷预测;建模
基金项目(Foundation):
作者(Author): 陈哲,张九根,陈浩
DOI: 10.16652/j.issn.1004-373x.2018.23.037
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